Generating a Navigational Map

ABSTRACT

Systems and methods are provided for vehicle navigation. In one implementation, a host vehicle-based sparse map feature harvester system may include at least one processor programmed to receive a plurality of images captured by a camera onboard the host vehicle as the host vehicle travels along a road segment in a first direction, wherein the plurality of images are representative of an environment of the host vehicle; detect one or more semantic features represented in one or more of the plurality of images, the one or more semantic features each being associated with a predetermined object type classification; identify at least one position descriptor associated with each of the detected one or more semantic features; identify three-dimensional feature points associated with one or more detected objects represented in at least one of the plurality of images; receive position information, for each of the plurality of images, wherein the position information is indicative of a position of the camera when each of the plurality of images was captured; and cause transmission of drive information for the road segment to an entity remotely-located relative to the host vehicle, wherein the drive information includes the identified at least one position descriptor associated with each of the detected one or more semantic features, the identified three-dimensional feature points, and the position information.

CROSS REFERENCES TO RELATED APPLICATIONS

This application claims the benefit of priority of U.S. ProvisionalApplication No. 62/956,987, filed on Jan. 3, 2020; U.S. ProvisionalApplication No. 62/956,993, filed on Jan. 3, 2020; U.S. ProvisionalApplication No. 62/956,997, filed on Jan. 3, 2020; United StatesProvisional Application No. 62/957,017, filed on Jan. 3, 2020; U.S.Provisional Application No. 62/957,019, filed on Jan. 3, 2020; U.S.Provisional Application No. 62/957,028, filed on Jan. 3, 2020; U.S.Provisional Application No. 63/119,293, filed on Nov. 30, 2020; U.S.Provisional Application No. 63/120,533, filed on Dec. 2, 2020; and U.S.Provisional Application No. 63/120,536, filed on Dec. 2, 2020. Theforegoing applications are incorporated herein by reference in theirentirety.

BACKGROUND Technical Field

The present disclosure relates generally to autonomous vehiclenavigation.

Background Information

As technology continues to advance, the goal of a fully autonomousvehicle that is capable of navigating on roadways is on the horizon.Autonomous vehicles may need to take into account a variety of factorsand make appropriate decisions based on those factors to safely andaccurately reach an intended destination. For example, an autonomousvehicle may need to process and interpret visual information (e.g.,information captured from a camera) and may also use informationobtained from other sources (e.g., from a GPS device, a speed sensor, anaccelerometer, a suspension sensor, etc.). At the same time, in order tonavigate to a destination, an autonomous vehicle may also need toidentify its location within a particular roadway (e.g., a specific lanewithin a multi-lane road), navigate alongside other vehicles, avoidobstacles and pedestrians, observe traffic signals and signs, and travelfrom one road to another road at appropriate intersections orinterchanges. Harnessing and interpreting vast volumes of informationcollected by an autonomous vehicle as the vehicle travels to itsdestination poses a multitude of design challenges. The sheer quantityof data (e.g., captured image data, map data, GPS data, sensor data,etc.) that an autonomous vehicle may need to analyze, access, and/orstore poses challenges that can in fact limit or even adversely affectautonomous navigation. Furthermore, if an autonomous vehicle relies ontraditional mapping technology to navigate, the sheer volume of dataneeded to store and update the map poses daunting challenges.

SUMMARY

Embodiments consistent with the present disclosure provide systems andmethods for autonomous vehicle navigation. The disclosed embodiments mayuse cameras to provide autonomous vehicle navigation features. Forexample, consistent with the disclosed embodiments, the disclosedsystems may include one, two, or more cameras that monitor theenvironment of a vehicle. The disclosed systems may provide anavigational response based on, for example, an analysis of imagescaptured by one or more of the cameras.

In an embodiment, a host vehicle-based sparse map feature harvestersystem may include at least one processor. The processor may beprogrammed to receive a plurality of images captured by a camera onboardthe host vehicle as the host vehicle travels along a road segment in afirst direction, wherein the plurality of images are representative ofan environment of the host vehicle; detect one or more semantic featuresrepresented in one or more of the plurality of images, the one or moresemantic features each being associated with a predetermined object typeclassification; identify at least one position descriptor associatedwith each of the detected one or more semantic features; and identifythree-dimensional feature points associated with one or more detectedobjects represented in at least one of the plurality of images. Theprocessor may further be programmed to receive position information, foreach of the plurality of images, wherein the position information isindicative of a position of the camera when each of the plurality ofimages was captured; and cause transmission of drive information for theroad segment to an entity remotely-located relative to the host vehicle,wherein the drive information includes the identified at least oneposition descriptor associated with each of the detected one or moresemantic features, the identified three-dimensional feature points, andthe position information.

In an embodiment, a method for harvesting sparse map features by a hostvehicle may include receiving a plurality of images captured by a cameraonboard the host vehicle as the host vehicle travels along a roadsegment in a first direction, wherein the plurality of images arerepresentative of an environment of the host vehicle; detecting one ormore semantic features represented in one or more of the plurality ofimages, the one or more semantic features each being associated with apredetermined object type classification; identifying at least oneposition descriptor associated with each of the detected one or moresemantic features; and identifying three-dimensional feature pointsassociated with a one or more detected objects represented in at leastone of the plurality of images. The method may further include receivingposition information, for each of the plurality of images, wherein theposition information is indicative of a position of the camera when eachof the plurality of images was captured; and causing transmission ofdrive information for the road segment to an entity remotely-locatedrelative to the host vehicle, wherein the drive information includes theidentified at least one position descriptor associated with each of thedetected one or more semantic features, the identified three-dimensionalfeature points, and the position information.

In an embodiment, a sparse map generator system for creating maps usedin navigating autonomous or partially autonomous vehicles may include atleast one processor. The processor may be programmed to receive firstdrive information for a road segment transmitted by a first plurality ofvehicles that traveled the road segment in a first direction, whereinthe first drive information includes a first plurality ofthree-dimensional feature points associated with objects detected bynavigation systems of the first plurality of vehicles; and receivesecond drive information for the road segment transmitted by a secondplurality of vehicles that traveled the road segment in a seconddirection opposite to the first direction, wherein the second driveinformation includes a second plurality of three dimensional featurepoints associated with objects detected by navigation systems of thesecond plurality of vehicles. The processor may further be programmed tocorrelate one or more of the first plurality of three-dimensionalfeature points with one or more of the second plurality ofthree-dimensional feature points; and generate a sparse map based on thecorrelation of the first plurality of three dimensional feature pointsand the second plurality of three-dimensional feature points, the sparsemap including at least a first target trajectory for a lane of travelalong the road segment in the first direction and at least a secondtarget trajectory for a lane of travel along the road segment in thesecond direction.

In an embodiment, a method for creating maps used in navigatingautonomous or partially autonomous vehicles may include receiving firstdrive information for a road segment transmitted by a first plurality ofvehicles that traveled the road segment in a first direction, whereinthe first drive information includes a first plurality ofthree-dimensional feature points associated with objects detected bynavigation systems of the first plurality of vehicles; receiving seconddrive information for the road segment transmitted by a second pluralityof vehicles that traveled the road segment in a second directionopposite to the first direction, wherein the second drive informationincludes a second plurality of three dimensional feature pointsassociated with objects detected by navigation systems of the secondplurality of vehicles; correlating one or more of the first plurality ofthree-dimensional feature points with one or more of the secondplurality of three-dimensional feature points; and generating a sparsemap based on the correlation of the first plurality of three dimensionalfeature points and the second plurality of three-dimensional featurepoints, the sparse map including at least a first target trajectory fora lane of travel along the road segment in the first direction and atleast a second target trajectory for a lane of travel along the roadsegment in the second direction.

In an embodiment, a navigation system for an autonomous or partiallyautonomous host vehicle may include at least one processor. Theprocessor may be programmed to receive, from an entity remotely locatedrelative to the host vehicle, a sparse map associated with at least oneroad segment. The sparse map may include a first plurality of mappednavigational features generated based on drive information previouslycollected from a first plurality of vehicles that traveled in a firstdirection along the at least one road segment, and a second plurality ofmapped navigational features generated based on drive informationpreviously collected from a second plurality of vehicles that traveledin a second direction along the at least one road segment, wherein thesecond direction is opposite to the first direction, and wherein thefirst plurality of mapped navigational features and the second pluralityof mapped navigational features are correlated within a commoncoordinate system. The processor may further be programmed to receive,from a camera associated with the host vehicle, a first plurality ofimages and a second plurality of images representative of an environmentof the host vehicle as the host vehicle travels along the at least oneroad segment in the first direction; determine a first navigationalaction for the host vehicle based on analysis of at least one of thefirst plurality of images and based on the first plurality of mappednavigational features; and cause one or more actuators associated withthe host vehicle to implement the first navigational action. Theprocessor may further be configured to determine a second navigationalaction for the host vehicle based on analysis of the second plurality ofimages and based on the second plurality of mapped navigationalfeatures; and cause the one or more actuators associated with the hostvehicle to implement the second navigational action.

In an embodiment, a method for navigating an autonomous or partiallyautonomous host vehicle may include receiving, from an entity remotelylocated relative to the host vehicle, a sparse map associated with atleast one road segment. The sparse map may include a first plurality ofmapped navigational features generated based on drive informationpreviously collected from a first plurality of vehicles that traveled ina first direction along the at least one road segment, and a secondplurality of mapped navigational features generated based on driveinformation previously collected from a second plurality of vehiclesthat traveled in a second direction along the at least one road segment,wherein the second direction is opposite to the first direction, andwherein the first plurality of mapped navigational features and thesecond plurality of mapped navigational features are correlated within acommon coordinate system. The method may further include receiving, froma camera associated with the host vehicle, a first plurality of imagesand a second plurality of images representative of an environment of thehost vehicle as the host vehicle travels along the at least one roadsegment in the first direction; determining a first navigational actionfor the host vehicle based on analysis of at least one of the firstplurality of images and based on the first plurality of mappednavigational features; and causing one or more actuators associated withthe host vehicle to implement the first navigational action. The methodmay further include determining a second navigational action for thehost vehicle based on analysis of the second plurality of images andbased on the second plurality of mapped navigational features; andcausing the one or more actuators associated with the host vehicle toimplement the second navigational action.

In an embodiment, a navigation system for creating maps used innavigating autonomous or partially autonomous vehicles may include atleast one processor. The processor may be programmed to receive driveinformation from each of a plurality of vehicles distributed across aplurality of vehicle groups that traverse a road junction, the roadjunction including a plurality of entrances and a plurality of exitsassociated with each of the plurality of entrances. Each vehicle groupmay include one or more vehicles that traverse a different entrance-exitcombination associated with the road junction. The drive informationfrom each of the plurality of vehicles may include three-dimensionalfeature points associated with objects detected by analyzing imagescaptured as a particular vehicle traversed a particular entrance-exitcombination of the road junction. The processor may further beprogrammed to, for each of the entrance-exit combinations, align thethree-dimensional feature points received in the drive informationcollected from the one or more vehicles that traversed thatentrance-exit combination to generate a plurality of alignedthree-dimensional feature point groups, one for each entrance-exitcombination of the road junction; correlate one or morethree-dimensional feature points in each of the plurality of alignedthree-dimensional feature point groups with one or morethree-dimensional feature points included in every other alignedthree-dimensional feature point group from among the plurality ofaligned three-dimensional feature point groups; and generate a sparsemap based on the correlation of the one or more three-dimensionalfeature points in each of the plurality of aligned three-dimensionalfeature point groups with one or more three-dimensional feature pointsincluded every other aligned three-dimensional feature point group, thesparse map including at least one target trajectory associated with eachof the entrance-exit combinations of the road junction.

In an embodiment, a method for creating maps used in navigatingautonomous or partially autonomous vehicles may include receiving driveinformation from each of a plurality of vehicles distributed across aplurality of vehicle groups that traverse a road junction, the roadjunction including a plurality of entrances and a plurality of exitsassociated with each of the plurality of entrances. Each vehicle groupincludes one or more vehicles that traverse a different entrance-exitcombination associated with the road junction. The drive informationfrom each of the plurality of vehicles includes three-dimensionalfeature points associated with objects detected by analyzing imagescaptured as a particular vehicle traversed a particular entrance-exitcombination of the road junction. The method may further include, foreach of the entrance-exit combinations, aligning the three-dimensionalfeature points received in the drive information collected from the oneor more vehicles that traversed that entrance-exit combination togenerate a plurality of aligned three-dimensional feature point groups,one for each entrance-exit combination of the road junction; correlatingone or more three-dimensional feature points in each of the plurality ofaligned three-dimensional feature point groups with one or morethree-dimensional feature points included in every other alignedthree-dimensional feature point group from among the plurality ofaligned three-dimensional feature point groups; and generating a sparsemap based on the correlation of the one or more three-dimensionalfeature points in each of the plurality of aligned three-dimensionalfeature point groups with one or more three-dimensional feature pointsincluded every other aligned three-dimensional feature point group, thesparse map including at least one target trajectory associated with eachof the entrance-exit combinations of the road junction.

In an embodiment, a host vehicle-based sparse map feature harvestersystem may include at least one processor. The processor may beprogrammed to receive a first image captured by a forward-facing cameraonboard the host vehicle, as the host vehicle travels along a roadsegment in a first direction, wherein the first image is representativeof an environment forward of the host vehicle; and receive a secondimage captured by a rearward-facing camera onboard the host vehicle, asthe host vehicle travels along the road segment in the first direction,wherein the second image is representative of an environment behind thehost vehicle. The processor may detect a first semantic featurerepresented in the first image, wherein the first semantic feature isassociated with a predetermined object type classification; identify atleast one position descriptor associated with the first semantic featurerepresented in the first image captured by the forward-facing camera;detect a second semantic feature represented in the second image,wherein the second semantic feature is associated with a predeterminedobject type classification; and identify at least one positiondescriptor associated with the second semantic feature represented inthe second image captured by the rearward-facing camera. The processormay then receive position information indicative of a position of theforward-facing camera when the first image was captured and indicativeof a position of the rearward-facing camera when the second image wascaptured; and cause transmission of drive information for the roadsegment to an entity remotely-located relative to the host vehicle,wherein the drive information includes the at least one positiondescriptor associated with the first semantic feature, the at least oneposition descriptor associated with the second semantic feature, and theposition information.

In an embodiment, a method for harvesting sparse map features by a hostvehicle may include receiving a first image captured by a forward-facingcamera onboard the host vehicle, as the host vehicle travels along aroad segment in a first direction, wherein the first image isrepresentative of an environment forward of the host vehicle; andreceiving a second image captured by a rearward-facing camera onboardthe host vehicle, as the host vehicle travels along the road segment inthe first direction, wherein the second image is representative of anenvironment behind the host vehicle. The method may further includedetecting a first semantic feature represented in the first image,wherein the first semantic feature is associated with a predeterminedobject type classification; identifying at least one position descriptorassociated with the first semantic feature represented in the firstimage captured by the forward-facing camera; detecting a second semanticfeature represented in the second image, wherein the second semanticfeature is associated with a predetermined object type classification;and identifying at least one position descriptor associated with thesecond semantic feature represented in the second image captured by therearward-facing camera. The method may further include receivingposition information indicative of a position of the forward-facingcamera when the first image was captured and indicative of a position ofthe rearward-facing camera when the second image was captured; andcausing transmission of drive information for the road segment to anentity remotely-located relative to the host vehicle, wherein the driveinformation includes the at least one position descriptor associatedwith the first semantic feature, the at least one position descriptorassociated with the second semantic feature, and the positioninformation.

In an embodiment, a sparse map generator system for creating maps usedin navigating autonomous or partially autonomous vehicles may include atleast one processor. The at least one processor may be programmed toreceive first drive information for a road segment transmitted by afirst plurality of vehicles that traveled the road segment in a firstdirection; receive second drive information for the road segmenttransmitted by a second plurality of vehicles that traveled the roadsegment in a second direction opposite to the first direction; andreceive, from at least one vehicle equipped with a forward-facing cameraand a rearward-facing camera, third drive information for the roadsegment, wherein the third drive information includes: a positiondescriptor associated with a first semantic feature detected based onanalysis of a forward-facing image captured by the forward-facingcamera; and a position descriptor associated with a second semanticfeature detected based on analysis of a rearward-facing image capturedby the rearward-facing camera. The processor may be programmed tocorrelate one or more aspects of the first drive information and thesecond drive information based, at least in part, upon the positiondescriptor associated with the first semantic feature detected based onanalysis of the forward-facing image and upon the position descriptorassociated with the second semantic feature detected based on analysisof the rearward-facing image captured by the rearward-facing camera; andgenerate the sparse map based, at least in part, on the correlation ofthe first drive information and the second drive information, the sparsemap including at least a first target trajectory for a lane of travelalong the road segment in the first direction and at least a secondtarget trajectory for a lane of travel along the road segment in thesecond direction.

In an embodiment, a method for creating maps used in navigatingautonomous or partially autonomous vehicles may include receiving firstdrive information for a road segment transmitted by a first plurality ofvehicles that traveled the road segment in a first direction; receivingsecond drive information for the road segment transmitted by a secondplurality of vehicles that traveled the road segment in a seconddirection opposite to the first direction; and receiving, from at leastone vehicle equipped with a forward-facing camera and a rearward-facingcamera, third drive information for the road segment, wherein the thirddrive information includes: a position descriptor associated with afirst semantic feature detected based on analysis of a forward-facingimage captured by the forward-facing camera; and a position descriptorassociated with a second semantic feature detected based on analysis ofa rearward-facing image captured by the rearward-facing camera. Themethod may further include correlating one or more aspects of the firstdrive information and the second drive information based, at least inpart, upon the position descriptor associated with the first semanticfeature detected based on analysis of the forward-facing image and uponthe position descriptor associated with the second semantic featuredetected based on analysis of the rearward-facing image captured by therearward-facing camera; and generating the sparse map based, at least inpart, on the correlation of the first drive information and the seconddrive information, the sparse map including at least a first targettrajectory for a lane of travel along the road segment in the firstdirection and at least a second target trajectory for a lane of travelalong the road segment in the second direction.

In an embodiment, a host vehicle-based sparse map feature harvestersystem may include at least one processor. The processor may beprogrammed to receive a first image captured by a forward-facing cameraonboard the host vehicle, as the host vehicle travels along a roadsegment in a first direction, wherein the first image is representativeof an environment forward of the host vehicle; and receive a secondimage captured by a rearward-facing camera onboard the host vehicle, asthe host vehicle travels along the road segment in the first direction,wherein the second image is representative of an environment behind thehost vehicle. The processor may further be programmed to detect at leastone object represented in the first image; identify at least one frontside two-dimensional feature point, the at least one front sidetwo-dimensional feature point being associated with the at least oneobject represented in the first image; detect a representation of the atleast one object in the second image; and identify at least one rearside two-dimensional feature point, the at least one rear sidetwo-dimensional feature point being associated with the at least oneobject represented in the second image. The processor may be programmedto receive position information indicative of a position of theforward-facing camera when the first image was captured and indicativeof a position of the rearward-facing camera when the second image wascaptured; and cause transmission of drive information for the roadsegment to an entity remotely-located relative to the host vehicle,wherein the drive information includes the at least one front sidetwo-dimensional feature point, the at least one rear sidetwo-dimensional feature point, and the position information.

In an embodiment, a method for harvesting sparse map features by a hostvehicle may include receiving a first image captured by a forward-facingcamera onboard the host vehicle, as the host vehicle travels along aroad segment in a first direction, wherein the first image isrepresentative of an environment forward of the host vehicle; andreceiving a second image captured by a rearward-facing camera onboardthe host vehicle, as the host vehicle travels along the road segment inthe first direction, wherein the second image is representative of anenvironment behind the host vehicle. The method may further includedetecting at least one object represented in the first image;identifying at least one front side two-dimensional feature point, theat least one front side two-dimensional feature point being associatedwith the at least one object represented in the first image; detecting arepresentation of the at least one object in the second image; andidentifying at least one rear side two-dimensional feature point, the atleast one rear side two-dimensional feature point being associated withthe at least one object represented in the second image. The method maythen include receiving position information indicative of a position ofthe forward-facing camera when the first image was captured andindicative of a position of the rearward-facing camera when the secondimage was captured; and causing transmission of drive information forthe road segment to an entity remotely-located relative to the hostvehicle, wherein the drive information includes the at least one frontside two-dimensional feature point, the at least one rear sidetwo-dimensional feature point, and the position information.

In an embodiment, a sparse map generator system for creating maps usedin navigating autonomous or partially autonomous vehicles may include atleast one processor. The processor may be programmed to receive firstdrive information for a road segment transmitted by a first plurality ofvehicles that traveled the road segment in a first direction; receivesecond drive information for the road segment transmitted by a secondplurality of vehicles that traveled the road segment in a seconddirection opposite to the first direction; and receive, from at leastone vehicle equipped with a forward-facing camera and a rearward-facingcamera, third drive information for the road segment, wherein the thirddrive information includes: at least one front side two-dimensionalfeature point generated based on analysis of a representation of anobject in a forward-facing image captured by the forward-facing camera;at least one rear side two-dimensional feature point generated based onanalysis of a representation of the object in a rearward-facing imagecaptured by the rearward-facing camera. The processor may further beprogrammed to correlate one or more aspects of the first driveinformation and the second drive information based, at least in part,upon the at least one front side two-dimensional feature point and uponthe at least one rear side two-dimensional feature point included in thethird drive information; and generate the sparse map based, at least inpart, on the correlation of the first drive information and the seconddrive information, the sparse map including at least a first targettrajectory for a lane of travel along the road segment in the firstdirection and at least a second target trajectory for a lane of travelalong the road segment in the second direction.

In an embodiment, a method for creating maps used in navigatingautonomous or partially autonomous vehicles may include receiving firstdrive information for a road segment transmitted by a first plurality ofvehicles that traveled the road segment in a first direction; receivingsecond drive information for the road segment transmitted by a secondplurality of vehicles that traveled the road segment in a seconddirection opposite to the first direction; and receiving, from at leastone vehicle equipped with a forward-facing camera and a rearward-facingcamera, third drive information for the road segment, wherein the thirddrive information includes: at least one front side two-dimensionalfeature point generated based on analysis of a representation of anobject in a forward-facing image captured by the forward-facing camera;at least one rear side two-dimensional feature point generated based onanalysis of a representation of the object in a rearward-facing imagecaptured by the rearward-facing camera. The method may further includecorrelating one or more aspects of the first drive information and thesecond drive information based, at least in part, upon the at least onefront side two-dimensional feature point and upon the at least one rearside two-dimensional feature point included in the third driveinformation; and generating the sparse map based, at least in part, onthe correlation of the first drive information and the second driveinformation, the sparse map including at least a first target trajectoryfor a lane of travel along the road segment in the first direction andat least a second target trajectory for a lane of travel along the roadsegment in the second direction.

Consistent with other disclosed embodiments, non-transitorycomputer-readable storage media may store program instructions, whichare executed by at least one processing device and perform any of themethods described herein.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of theclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate various disclosed embodiments. Inthe drawings:

FIG. 1 is a diagrammatic representation of an exemplary systemconsistent with the disclosed embodiments.

FIG. 2A is a diagrammatic side view representation of an exemplaryvehicle including a system consistent with the disclosed embodiments.

FIG. 2B is a diagrammatic top view representation of the vehicle andsystem shown in FIG. 2A consistent with the disclosed embodiments.

FIG. 2C is a diagrammatic top view representation of another embodimentof a vehicle including a system consistent with the disclosedembodiments.

FIG. 2D is a diagrammatic top view representation of yet anotherembodiment of a vehicle including a system consistent with the disclosedembodiments.

FIG. 2E is a diagrammatic top view representation of yet anotherembodiment of a vehicle including a system consistent with the disclosedembodiments.

FIG. 2F is a diagrammatic representation of exemplary vehicle controlsystems consistent with the disclosed embodiments.

FIG. 3A is a diagrammatic representation of an interior of a vehicleincluding a rearview mirror and a user interface for a vehicle imagingsystem consistent with the disclosed embodiments.

FIG. 3B is an illustration of an example of a camera mount that isconfigured to be positioned behind a rearview mirror and against avehicle windshield consistent with the disclosed embodiments.

FIG. 3C is an illustration of the camera mount shown in FIG. 3B from adifferent perspective consistent with the disclosed embodiments.

FIG. 3D is an illustration of an example of a camera mount that isconfigured to be positioned behind a rearview mirror and against avehicle windshield consistent with the disclosed embodiments.

FIG. 4 is an exemplary block diagram of a memory configured to storeinstructions for performing one or more operations consistent with thedisclosed embodiments.

FIG. 5A is a flowchart showing an exemplary process for causing one ormore navigational responses based on monocular image analysis consistentwith disclosed embodiments.

FIG. 5B is a flowchart showing an exemplary process for detecting one ormore vehicles and/or pedestrians in a set of images consistent with thedisclosed embodiments.

FIG. 5C is a flowchart showing an exemplary process for detecting roadmarks and/or lane geometry information in a set of images consistentwith the disclosed embodiments.

FIG. 5D is a flowchart showing an exemplary process for detectingtraffic lights in a set of images consistent with the disclosedembodiments.

FIG. 5E is a flowchart showing an exemplary process for causing one ormore navigational responses based on a vehicle path consistent with thedisclosed embodiments.

FIG. 5F is a flowchart showing an exemplary process for determiningwhether a leading vehicle is changing lanes consistent with thedisclosed embodiments.

FIG. 6 is a flowchart showing an exemplary process for causing one ormore navigational responses based on stereo image analysis consistentwith the disclosed embodiments.

FIG. 7 is a flowchart showing an exemplary process for causing one ormore navigational responses based on an analysis of three sets of imagesconsistent with the disclosed embodiments.

FIG. 8 shows a sparse map for providing autonomous vehicle navigation,consistent with the disclosed embodiments.

FIG. 9A illustrates a polynomial representation of a portions of a roadsegment consistent with the disclosed embodiments.

FIG. 9B illustrates a curve in three-dimensional space representing atarget trajectory of a vehicle, for a particular road segment, includedin a sparse map consistent with the disclosed embodiments.

FIG. 10 illustrates example landmarks that may be included in sparse mapconsistent with the disclosed embodiments.

FIG. 11A shows polynomial representations of trajectories consistentwith the disclosed embodiments.

FIGS. 11B and 11C show target trajectories along a multi-lane roadconsistent with disclosed embodiments.

FIG. 11D shows an example road signature profile consistent withdisclosed embodiments.

FIG. 12 is a schematic illustration of a system that uses crowd sourcingdata received from a plurality of vehicles for autonomous vehiclenavigation, consistent with the disclosed embodiments.

FIG. 13 illustrates an example autonomous vehicle road navigation modelrepresented by a plurality of three dimensional splines, consistent withthe disclosed embodiments.

FIG. 14 shows a map skeleton generated from combining locationinformation from many drives, consistent with the disclosed embodiments.

FIG. 15 shows an example of a longitudinal alignment of two drives withexample signs as landmarks, consistent with the disclosed embodiments.

FIG. 16 shows an example of a longitudinal alignment of many drives withan example sign as a landmark, consistent with the disclosedembodiments.

FIG. 17 is a schematic illustration of a system for generating drivedata using a camera, a vehicle, and a server, consistent with thedisclosed embodiments.

FIG. 18 is a schematic illustration of a system for crowdsourcing asparse map, consistent with the disclosed embodiments.

FIG. 19 is a flowchart showing an exemplary process for generating asparse map for autonomous vehicle navigation along a road segment,consistent with the disclosed embodiments.

FIG. 20 illustrates a block diagram of a server consistent with thedisclosed embodiments.

FIG. 21 illustrates a block diagram of a memory consistent with thedisclosed embodiments.

FIG. 22 illustrates a process of clustering vehicle trajectoriesassociated with vehicles, consistent with the disclosed embodiments.

FIG. 23 illustrates a navigation system for a vehicle, which may be usedfor autonomous navigation, consistent with the disclosed embodiments.

FIGS. 24A, 24B, 24C, and 24D illustrate exemplary lane marks that may bedetected consistent with the disclosed embodiments.

FIG. 24E shows exemplary mapped lane marks consistent with the disclosedembodiments.

FIG. 24F shows an exemplary anomaly associated with detecting a lanemark consistent with the disclosed embodiments.

FIG. 25A shows an exemplary image of a vehicle's surrounding environmentfor navigation based on the mapped lane marks consistent with thedisclosed embodiments.

FIG. 25B illustrates a lateral localization correction of a vehiclebased on mapped lane marks in a road navigation model consistent withthe disclosed embodiments.

FIGS. 25C and 25D provide conceptual representations of a localizationtechnique for locating a host vehicle along a target trajectory usingmapped features included in a sparse map.

FIG. 26A is a flowchart showing an exemplary process for mapping a lanemark for use in autonomous vehicle navigation consistent with disclosedembodiments.

FIG. 26B is a flowchart showing an exemplary process for autonomouslynavigating a host vehicle along a road segment using mapped lane marksconsistent with disclosed embodiments.

FIG. 27 illustrates an example image that may be captured by a hostvehicle for aligning drive information, consistent with the disclosedembodiments.

FIG. 28 illustrates an example 3D point that may be obtained by a hostvehicle, consistent with the disclosed embodiments.

FIG. 29 illustrates an example system for generating a sparse map basedon 3D point clouds, consistent with the disclosed embodiments.

FIG. 30 illustrates an example alignment process that may be performedfor points captured from different drive directions, consistent with thedisclosed embodiments.

FIG. 31 is a flowchart showing an example process for harvesting datafor a sparse map, consistent with the disclosed embodiments.

FIG. 32 is a flowchart showing an example process for creating maps usedin navigating autonomous or partially autonomous vehicles, consistentwith the disclosed embodiments.

FIG. 33 is an illustration of an example road segment along which a hostvehicle may navigate, consistent with the disclosed embodiments.

FIG. 34 illustrates an example sparse map having two-way alignedtrajectories, consistent with the disclosed embodiments.

FIG. 35 is a flowchart showing an example process for navigating anautonomous or partially autonomous host vehicle, consistent with thedisclosed embodiments.

FIG. 36A illustrates an example junction that may be traversed by one ormore vehicles, consistent with the disclosed embodiments.

FIG. 36B illustrates various entrance and exit combinations for ajunction that may be traveled by a host vehicle, consistent with thedisclosed embodiments.

FIG. 37 illustrates example three-dimensional points that may becollected by a host vehicle while traversing a junction, consistent withthe disclosed embodiments.

FIG. 38 illustrates an example alignment of three-dimensional pointscollected along a common target trajectory, consistent with thedisclosed embodiments.

FIG. 39 illustrates an example process for generating a sparse map basedon correlated 3D points from multiple entrance-exit combinations,consistent with the disclosed embodiments.

FIG. 40 illustrates an example alignment of two sets of aligned 3Dpoints, consistent with the disclosed embodiments.

FIG. 41 is a flowchart showing an example process for creating maps usedin navigating autonomous or partially autonomous vehicles, consistentwith the disclosed embodiments.

FIG. 42A illustrates an example road segment along which driveinformation from multiple directions may be aligned using rear-facingcameras, consistent with the disclosed embodiments.

FIG. 42B illustrates an example image that may be captured by afront-facing camera of a host vehicle, consistent with the disclosedembodiments.

FIG. 43A illustrates an example scenario for a host vehicle to capture arear-facing image along a road segment, consistent with the disclosedembodiments.

FIG. 43B illustrates an example rear-facing image that may be taken by ahost vehicle, consistent with the disclosed embodiments.

FIG. 44 illustrates example sets of drive information captured byvehicles traveling in opposite directions along a road segment,consistent with the disclosed embodiments.

FIG. 45 is a flowchart showing an example process for harvesting datafor a sparse map, consistent with the disclosed embodiments.

FIG. 46 is a flowchart showing an example process for creating maps usedin navigating autonomous or partially autonomous vehicles, consistentwith the disclosed embodiments.

FIG. 47 is a flowchart showing an example process for harvesting datafor a sparse map, consistent with the disclosed embodiments.

FIG. 48 is a flowchart showing an example process for creating maps usedin navigating autonomous or partially autonomous vehicles, consistentwith the disclosed embodiments.

FIG. 49 illustrates an exemplary system for automatically generating anavigational map relative to one or more road segments, consistent withthe disclosed embodiments.

FIGS. 50A, 50B, and 50C illustrate an exemplary process for collectingnavigational information, consistent with disclosed embodiments.

FIG. 51 is a flowchart showing an exemplary process for automaticallygenerating a navigational map relative to one or more road segments,consistent with the disclosed embodiments.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description to refer to the same or similar parts.While several illustrative embodiments are described herein,modifications, adaptations and other implementations are possible. Forexample, substitutions, additions or modifications may be made to thecomponents illustrated in the drawings, and the illustrative methodsdescribed herein may be modified by substituting, reordering, removing,or adding steps to the disclosed methods. Accordingly, the followingdetailed description is not limited to the disclosed embodiments andexamples. Instead, the proper scope is defined by the appended claims.

Autonomous Vehicle Overview

As used throughout this disclosure, the term “autonomous vehicle” refersto a vehicle capable of implementing at least one navigational changewithout driver input. A “navigational change” refers to a change in oneor more of steering, braking, or acceleration of the vehicle. To beautonomous, a vehicle need not be fully automatic (e.g., fully operationwithout a driver or without driver input). Rather, an autonomous vehicleincludes those that can operate under driver control during certain timeperiods and without driver control during other time periods. Autonomousvehicles may also include vehicles that control only some aspects ofvehicle navigation, such as steering (e.g., to maintain a vehicle coursebetween vehicle lane constraints), but may leave other aspects to thedriver (e.g., braking). In some cases, autonomous vehicles may handlesome or all aspects of braking, speed control, and/or steering of thevehicle.

As human drivers typically rely on visual cues and observations tocontrol a vehicle, transportation infrastructures are built accordingly,with lane markings, traffic signs, and traffic lights are all designedto provide visual information to drivers. In view of these designcharacteristics of transportation infrastructures, an autonomous vehiclemay include a camera and a processing unit that analyzes visualinformation captured from the environment of the vehicle. The visualinformation may include, for example, components of the transportationinfrastructure (e.g., lane markings, traffic signs, traffic lights,etc.) that are observable by drivers and other obstacles (e.g., othervehicles, pedestrians, debris, etc.). Additionally, an autonomousvehicle may also use stored information, such as information thatprovides a model of the vehicle's environment when navigating. Forexample, the vehicle may use GPS data, sensor data (e.g., from anaccelerometer, a speed sensor, a suspension sensor, etc.), and/or othermap data to provide information related to its environment while thevehicle is traveling, and the vehicle (as well as other vehicles) mayuse the information to localize itself on the model.

In some embodiments in this disclosure, an autonomous vehicle may useinformation obtained while navigating (e.g., from a camera, GPS device,an accelerometer, a speed sensor, a suspension sensor, etc.). In otherembodiments, an autonomous vehicle may use information obtained frompast navigations by the vehicle (or by other vehicles) while navigating.In yet other embodiments, an autonomous vehicle may use a combination ofinformation obtained while navigating and information obtained from pastnavigations. The following sections provide an overview of a systemconsistent with the disclosed embodiments, followed by an overview of aforward-facing imaging system and methods consistent with the system.The sections that follow disclose systems and methods for constructing,using, and updating a sparse map for autonomous vehicle navigation.

System Overview

FIG. 1 is a block diagram representation of a system 100 consistent withthe exemplary disclosed embodiments. System 100 may include variouscomponents depending on the requirements of a particular implementation.In some embodiments, system 100 may include a processing unit 110, animage acquisition unit 120, a position sensor 130, one or more memoryunits 140, 150, a map database 160, a user interface 170, and a wirelesstransceiver 172. Processing unit 110 may include one or more processingdevices. In some embodiments, processing unit 110 may include anapplications processor 180, an image processor 190, or any othersuitable processing device. Similarly, image acquisition unit 120 mayinclude any number of image acquisition devices and components dependingon the requirements of a particular application. In some embodiments,image acquisition unit 120 may include one or more image capture devices(e.g., cameras), such as image capture device 122, image capture device124, and image capture device 126. System 100 may also include a datainterface 128 communicatively connecting processing device 110 to imageacquisition device 120. For example, data interface 128 may include anywired and/or wireless link or links for transmitting image data acquiredby image accusation device 120 to processing unit 110.

Wireless transceiver 172 may include one or more devices configured toexchange transmissions over an air interface to one or more networks(e.g., cellular, the Internet, etc.) by use of a radio frequency,infrared frequency, magnetic field, or an electric field. Wirelesstransceiver 172 may use any known standard to transmit and/or receivedata (e.g., Wi-Fi, Bluetooth®, Bluetooth Smart, 802.15.4, ZigBee, etc.).Such transmissions can include communications from the host vehicle toone or more remotely located servers. Such transmissions may alsoinclude communications (one-way or two-way) between the host vehicle andone or more target vehicles in an environment of the host vehicle (e.g.,to facilitate coordination of navigation of the host vehicle in view ofor together with target vehicles in the environment of the hostvehicle), or even a broadcast transmission to unspecified recipients ina vicinity of the transmitting vehicle.

Both applications processor 180 and image processor 190 may includevarious types of processing devices. For example, either or both ofapplications processor 180 and image processor 190 may include amicroprocessor, preprocessors (such as an image preprocessor), agraphics processing unit (GPU), a central processing unit (CPU), supportcircuits, digital signal processors, integrated circuits, memory, or anyother types of devices suitable for running applications and for imageprocessing and analysis. In some embodiments, applications processor 180and/or image processor 190 may include any type of single or multi-coreprocessor, mobile device microcontroller, central processing unit, etc.Various processing devices may be used, including, for example,processors available from manufacturers such as Intel®, AMD®, etc., orGPUs available from manufacturers such as NVIDIA®, ATI®, etc. and mayinclude various architectures (e.g., x86 processor, ARM®, etc.).

In some embodiments, applications processor 180 and/or image processor190 may include any of the EyeQ series of processor chips available fromMobileye®. These processor designs each include multiple processingunits with local memory and instruction sets. Such processors mayinclude video inputs for receiving image data from multiple imagesensors and may also include video out capabilities. In one example, theEyeQ2® uses 90 nm-micron technology operating at 332 Mhz. The EyeQ2®architecture consists of two floating point, hyper-thread 32-bit RISCCPUs (MIPS32® 34K® cores), five Vision Computing Engines (VCE), threeVector Microcode Processors (VMP®), Denali 64-bit Mobile DDR Controller,128-bit internal Sonics Interconnect, dual 16-bit Video input and 18-bitVideo output controllers, 16 channels DMA and several peripherals. TheMIPS34K CPU manages the five VCEs, three VMP™ and the DMA, the secondMIPS34K CPU and the multi-channel DMA as well as the other peripherals.The five VCEs, three VMP® and the MIPS34K CPU can perform intensivevision computations required by multi-function bundle applications. Inanother example, the EyeQ3®, which is a third generation processor andis six times more powerful that the EyeQ2®, may be used in the disclosedembodiments. In other examples, the EyeQ4® and/or the EyeQ5® may be usedin the disclosed embodiments. Of course, any newer or future EyeQprocessing devices may also be used together with the disclosedembodiments.

Any of the processing devices disclosed herein may be configured toperform certain functions. Configuring a processing device, such as anyof the described EyeQ processors or other controller or microprocessor,to perform certain functions may include programming of computerexecutable instructions and making those instructions available to theprocessing device for execution during operation of the processingdevice. In some embodiments, configuring a processing device may includeprogramming the processing device directly with architecturalinstructions. For example, processing devices such as field-programmablegate arrays (FPGAs), application-specific integrated circuits (ASICs),and the like may be configured using, for example, one or more hardwaredescription languages (HDLs).

In other embodiments, configuring a processing device may includestoring executable instructions on a memory that is accessible to theprocessing device during operation. For example, the processing devicemay access the memory to obtain and execute the stored instructionsduring operation. In either case, the processing device configured toperform the sensing, image analysis, and/or navigational functionsdisclosed herein represents a specialized hardware-based system incontrol of multiple hardware based components of a host vehicle.

While FIG. 1 depicts two separate processing devices included inprocessing unit 110, more or fewer processing devices may be used. Forexample, in some embodiments, a single processing device may be used toaccomplish the tasks of applications processor 180 and image processor190. In other embodiments, these tasks may be performed by more than twoprocessing devices. Further, in some embodiments, system 100 may includeone or more of processing unit 110 without including other components,such as image acquisition unit 120.

Processing unit 110 may comprise various types of devices. For example,processing unit 110 may include various devices, such as a controller,an image preprocessor, a central processing unit (CPU), a graphicsprocessing unit (GPU), support circuits, digital signal processors,integrated circuits, memory, or any other types of devices for imageprocessing and analysis. The image preprocessor may include a videoprocessor for capturing, digitizing and processing the imagery from theimage sensors. The CPU may comprise any number of microcontrollers ormicroprocessors. The GPU may also comprise any number ofmicrocontrollers or microprocessors. The support circuits may be anynumber of circuits generally well known in the art, including cache,power supply, clock and input-output circuits. The memory may storesoftware that, when executed by the processor, controls the operation ofthe system. The memory may include databases and image processingsoftware. The memory may comprise any number of random access memories,read only memories, flash memories, disk drives, optical storage, tapestorage, removable storage and other types of storage. In one instance,the memory may be separate from the processing unit 110. In anotherinstance, the memory may be integrated into the processing unit 110.

Each memory 140, 150 may include software instructions that whenexecuted by a processor (e.g., applications processor 180 and/or imageprocessor 190), may control operation of various aspects of system 100.These memory units may include various databases and image processingsoftware, as well as a trained system, such as a neural network, or adeep neural network, for example. The memory units may include randomaccess memory (RAM), read only memory (ROM), flash memory, disk drives,optical storage, tape storage, removable storage and/or any other typesof storage. In some embodiments, memory units 140, 150 may be separatefrom the applications processor 180 and/or image processor 190. In otherembodiments, these memory units may be integrated into applicationsprocessor 180 and/or image processor 190.

Position sensor 130 may include any type of device suitable fordetermining a location associated with at least one component of system100. In some embodiments, position sensor 130 may include a GPSreceiver. Such receivers can determine a user position and velocity byprocessing signals broadcasted by global positioning system satellites.Position information from position sensor 130 may be made available toapplications processor 180 and/or image processor 190.

In some embodiments, system 100 may include components such as a speedsensor (e.g., a tachometer, a speedometer) for measuring a speed ofvehicle 200 and/or an accelerometer (either single axis or multiaxis)for measuring acceleration of vehicle 200.

User interface 170 may include any device suitable for providinginformation to or for receiving inputs from one or more users of system100. In some embodiments, user interface 170 may include user inputdevices, including, for example, a touchscreen, microphone, keyboard,pointer devices, track wheels, cameras, knobs, buttons, etc. With suchinput devices, a user may be able to provide information inputs orcommands to system 100 by typing instructions or information, providingvoice commands, selecting menu options on a screen using buttons,pointers, or eye-tracking capabilities, or through any other suitabletechniques for communicating information to system 100.

User interface 170 may be equipped with one or more processing devicesconfigured to provide and receive information to or from a user andprocess that information for use by, for example, applications processor180. In some embodiments, such processing devices may executeinstructions for recognizing and tracking eye movements, receiving andinterpreting voice commands, recognizing and interpreting touches and/orgestures made on a touchscreen, responding to keyboard entries or menuselections, etc. In some embodiments, user interface 170 may include adisplay, speaker, tactile device, and/or any other devices for providingoutput information to a user.

Map database 160 may include any type of database for storing map datauseful to system 100. In some embodiments, map database 160 may includedata relating to the position, in a reference coordinate system, ofvarious items, including roads, water features, geographic features,businesses, points of interest, restaurants, gas stations, etc. Mapdatabase 160 may store not only the locations of such items, but alsodescriptors relating to those items, including, for example, namesassociated with any of the stored features. In some embodiments, mapdatabase 160 may be physically located with other components of system100. Alternatively or additionally, map database 160 or a portionthereof may be located remotely with respect to other components ofsystem 100 (e.g., processing unit 110). In such embodiments, informationfrom map database 160 may be downloaded over a wired or wireless dataconnection to a network (e.g., over a cellular network and/or theInternet, etc.). In some cases, map database 160 may store a sparse datamodel including polynomial representations of certain road features(e.g., lane markings) or target trajectories for the host vehicle.Systems and methods of generating such a map are discussed below withreferences to FIGS. 8-19.

Image capture devices 122, 124, and 126 may each include any type ofdevice suitable for capturing at least one image from an environment.Moreover, any number of image capture devices may be used to acquireimages for input to the image processor. Some embodiments may includeonly a single image capture device, while other embodiments may includetwo, three, or even four or more image capture devices. Image capturedevices 122, 124, and 126 will be further described with reference toFIGS. 2B-2E, below.

System 100, or various components thereof, may be incorporated intovarious different platforms. In some embodiments, system 100 may beincluded on a vehicle 200, as shown in FIG. 2A. For example, vehicle 200may be equipped with a processing unit 110 and any of the othercomponents of system 100, as described above relative to FIG. 1. Whilein some embodiments vehicle 200 may be equipped with only a single imagecapture device (e.g., camera), in other embodiments, such as thosediscussed in connection with FIGS. 2B-2E, multiple image capture devicesmay be used. For example, either of image capture devices 122 and 124 ofvehicle 200, as shown in FIG. 2A, may be part of an ADAS (AdvancedDriver Assistance Systems) imaging set.

The image capture devices included on vehicle 200 as part of the imageacquisition unit 120 may be positioned at any suitable location. In someembodiments, as shown in FIGS. 2A-2E and 3A-3C, image capture device 122may be located in the vicinity of the rearview mirror. This position mayprovide a line of sight similar to that of the driver of vehicle 200,which may aid in determining what is and is not visible to the driver.Image capture device 122 may be positioned at any location near therearview mirror, but placing image capture device 122 on the driver sideof the mirror may further aid in obtaining images representative of thedriver's field of view and/or line of sight.

Other locations for the image capture devices of image acquisition unit120 may also be used. For example, image capture device 124 may belocated on or in a bumper of vehicle 200. Such a location may beespecially suitable for image capture devices having a wide field ofview. The line of sight of bumper-located image capture devices can bedifferent from that of the driver and, therefore, the bumper imagecapture device and driver may not always see the same objects. The imagecapture devices (e.g., image capture devices 122, 124, and 126) may alsobe located in other locations. For example, the image capture devicesmay be located on or in one or both of the side mirrors of vehicle 200,on the roof of vehicle 200, on the hood of vehicle 200, on the trunk ofvehicle 200, on the sides of vehicle 200, mounted on, positioned behind,or positioned in front of any of the windows of vehicle 200, and mountedin or near light figures on the front and/or back of vehicle 200, etc.

In addition to image capture devices, vehicle 200 may include variousother components of system 100. For example, processing unit 110 may beincluded on vehicle 200 either integrated with or separate from anengine control unit (ECU) of the vehicle. Vehicle 200 may also beequipped with a position sensor 130, such as a GPS receiver and may alsoinclude a map database 160 and memory units 140 and 150.

As discussed earlier, wireless transceiver 172 may and/or receive dataover one or more networks (e.g., cellular networks, the Internet, etc.).For example, wireless transceiver 172 may upload data collected bysystem 100 to one or more servers, and download data from the one ormore servers. Via wireless transceiver 172, system 100 may receive, forexample, periodic or on demand updates to data stored in map database160, memory 140, and/or memory 150. Similarly, wireless transceiver 172may upload any data (e.g., images captured by image acquisition unit120, data received by position sensor 130 or other sensors, vehiclecontrol systems, etc.) from by system 100 and/or any data processed byprocessing unit 110 to the one or more servers.

System 100 may upload data to a server (e.g., to the cloud) based on aprivacy level setting. For example, system 100 may implement privacylevel settings to regulate or limit the types of data (includingmetadata) sent to the server that may uniquely identify a vehicle and ordriver/owner of a vehicle. Such settings may be set by user via, forexample, wireless transceiver 172, be initialized by factory defaultsettings, or by data received by wireless transceiver 172.

In some embodiments, system 100 may upload data according to a “high”privacy level, and under setting a setting, system 100 may transmit data(e.g., location information related to a route, captured images, etc.)without any details about the specific vehicle and/or driver/owner. Forexample, when uploading data according to a “high” privacy setting,system 100 may not include a vehicle identification number (VIN) or aname of a driver or owner of the vehicle, and may instead of transmitdata, such as captured images and/or limited location informationrelated to a route.

Other privacy levels are contemplated. For example, system 100 maytransmit data to a server according to an “intermediate” privacy leveland include additional information not included under a “high” privacylevel, such as a make and/or model of a vehicle and/or a vehicle type(e.g., a passenger vehicle, sport utility vehicle, truck, etc.). In someembodiments, system 100 may upload data according to a “low” privacylevel. Under a “low” privacy level setting, system 100 may upload dataand include information sufficient to uniquely identify a specificvehicle, owner/driver, and/or a portion or entirely of a route traveledby the vehicle. Such “low” privacy level data may include one or moreof, for example, a VIN, a driver/owner name, an origination point of avehicle prior to departure, an intended destination of the vehicle, amake and/or model of the vehicle, a type of the vehicle, etc.

FIG. 2A is a diagrammatic side view representation of an exemplaryvehicle imaging system consistent with the disclosed embodiments. FIG.2B is a diagrammatic top view illustration of the embodiment shown inFIG. 2A. As illustrated in FIG. 2B, the disclosed embodiments mayinclude a vehicle 200 including in its body a system 100 with a firstimage capture device 122 positioned in the vicinity of the rearviewmirror and/or near the driver of vehicle 200, a second image capturedevice 124 positioned on or in a bumper region (e.g., one of bumperregions 210) of vehicle 200, and a processing unit 110.

As illustrated in FIG. 2C, image capture devices 122 and 124 may both bepositioned in the vicinity of the rearview mirror and/or near the driverof vehicle 200. Additionally, while two image capture devices 122 and124 are shown in FIGS. 2B and 2C, it should be understood that otherembodiments may include more than two image capture devices. Forexample, in the embodiments shown in FIGS. 2D and 2E, first, second, andthird image capture devices 122, 124, and 126, are included in thesystem 100 of vehicle 200.

As illustrated in FIG. 2D, image capture device 122 may be positioned inthe vicinity of the rearview mirror and/or near the driver of vehicle200, and image capture devices 124 and 126 may be positioned on or in abumper region (e.g., one of bumper regions 210) of vehicle 200. And asshown in FIG. 2E, image capture devices 122, 124, and 126 may bepositioned in the vicinity of the rearview mirror and/or near the driverseat of vehicle 200. The disclosed embodiments are not limited to anyparticular number and configuration of the image capture devices, andthe image capture devices may be positioned in any appropriate locationwithin and/or on vehicle 200.

It is to be understood that the disclosed embodiments are not limited tovehicles and could be applied in other contexts. It is also to beunderstood that disclosed embodiments are not limited to a particulartype of vehicle 200 and may be applicable to all types of vehiclesincluding automobiles, trucks, trailers, and other types of vehicles.

The first image capture device 122 may include any suitable type ofimage capture device. Image capture device 122 may include an opticalaxis. In one instance, the image capture device 122 may include anAptina M9V024 WVGA sensor with a global shutter. In other embodiments,image capture device 122 may provide a resolution of 1280×960 pixels andmay include a rolling shutter. Image capture device 122 may includevarious optical elements. In some embodiments one or more lenses may beincluded, for example, to provide a desired focal length and field ofview for the image capture device. In some embodiments, image capturedevice 122 may be associated with a 6 mm lens or a 12 mm lens. In someembodiments, image capture device 122 may be configured to captureimages having a desired field-of-view (FOV) 202, as illustrated in FIG.2D. For example, image capture device 122 may be configured to have aregular FOV, such as within a range of 40 degrees to 56 degrees,including a 46 degree FOV, 50 degree FOV, 52 degree FOV, or greater.Alternatively, image capture device 122 may be configured to have anarrow FOV in the range of 23 to 40 degrees, such as a 28 degree FOV or36 degree FOV. In addition, image capture device 122 may be configuredto have a wide FOV in the range of 100 to 180 degrees. In someembodiments, image capture device 122 may include a wide angle bumpercamera or one with up to a 180 degree FOV. In some embodiments, imagecapture device 122 may be a 7.2M pixel image capture device with anaspect ratio of about 2:1 (e.g., H×V=3800×1900 pixels) with about 100degree horizontal FOV. Such an image capture device may be used in placeof a three image capture device configuration. Due to significant lensdistortion, the vertical FOV of such an image capture device may besignificantly less than 50 degrees in implementations in which the imagecapture device uses a radially symmetric lens. For example, such a lensmay not be radially symmetric which would allow for a vertical FOVgreater than 50 degrees with 100 degree horizontal FOV.

The first image capture device 122 may acquire a plurality of firstimages relative to a scene associated with the vehicle 200. Each of theplurality of first images may be acquired as a series of image scanlines, which may be captured using a rolling shutter. Each scan line mayinclude a plurality of pixels.

The first image capture device 122 may have a scan rate associated withacquisition of each of the first series of image scan lines. The scanrate may refer to a rate at which an image sensor can acquire image dataassociated with each pixel included in a particular scan line.

Image capture devices 122, 124, and 126 may contain any suitable typeand number of image sensors, including CCD sensors or CMOS sensors, forexample. In one embodiment, a CMOS image sensor may be employed alongwith a rolling shutter, such that each pixel in a row is read one at atime, and scanning of the rows proceeds on a row-by-row basis until anentire image frame has been captured. In some embodiments, the rows maybe captured sequentially from top to bottom relative to the frame.

In some embodiments, one or more of the image capture devices (e.g.,image capture devices 122, 124, and 126) disclosed herein may constitutea high resolution imager and may have a resolution greater than 5Mpixel, 7M pixel, 10M pixel, or greater.

The use of a rolling shutter may result in pixels in different rowsbeing exposed and captured at different times, which may cause skew andother image artifacts in the captured image frame. On the other hand,when the image capture device 122 is configured to operate with a globalor synchronous shutter, all of the pixels may be exposed for the sameamount of time and during a common exposure period. As a result, theimage data in a frame collected from a system employing a global shutterrepresents a snapshot of the entire FOV (such as FOV 202) at aparticular time. In contrast, in a rolling shutter application, each rowin a frame is exposed and data is capture at different times. Thus,moving objects may appear distorted in an image capture device having arolling shutter. This phenomenon will be described in greater detailbelow.

The second image capture device 124 and the third image capturing device126 may be any type of image capture device. Like the first imagecapture device 122, each of image capture devices 124 and 126 mayinclude an optical axis. In one embodiment, each of image capturedevices 124 and 126 may include an Aptina M9V024 WVGA sensor with aglobal shutter. Alternatively, each of image capture devices 124 and 126may include a rolling shutter. Like image capture device 122, imagecapture devices 124 and 126 may be configured to include various lensesand optical elements. In some embodiments, lenses associated with imagecapture devices 124 and 126 may provide FOVs (such as FOVs 204 and 206)that are the same as, or narrower than, a FOV (such as FOV 202)associated with image capture device 122. For example, image capturedevices 124 and 126 may have FOVs of 40 degrees, 30 degrees, 26 degrees,23 degrees, 20 degrees, or less.

Image capture devices 124 and 126 may acquire a plurality of second andthird images relative to a scene associated with the vehicle 200. Eachof the plurality of second and third images may be acquired as a secondand third series of image scan lines, which may be captured using arolling shutter. Each scan line or row may have a plurality of pixels.Image capture devices 124 and 126 may have second and third scan ratesassociated with acquisition of each of image scan lines included in thesecond and third series.

Each image capture device 122, 124, and 126 may be positioned at anysuitable position and orientation relative to vehicle 200. The relativepositioning of the image capture devices 122, 124, and 126 may beselected to aid in fusing together the information acquired from theimage capture devices. For example, in some embodiments, a FOV (such asFOV 204) associated with image capture device 124 may overlap partiallyor fully with a FOV (such as FOV 202) associated with image capturedevice 122 and a FOV (such as FOV 206) associated with image capturedevice 126.

Image capture devices 122, 124, and 126 may be located on vehicle 200 atany suitable relative heights. In one instance, there may be a heightdifference between the image capture devices 122, 124, and 126, whichmay provide sufficient parallax information to enable stereo analysis.For example, as shown in FIG. 2A, the two image capture devices 122 and124 are at different heights. There may also be a lateral displacementdifference between image capture devices 122, 124, and 126, givingadditional parallax information for stereo analysis by processing unit110, for example. The difference in the lateral displacement may bedenoted by d_(x), as shown in FIGS. 2C and 2D. In some embodiments, foreor aft displacement (e.g., range displacement) may exist between imagecapture devices 122, 124, and 126. For example, image capture device 122may be located 0.5 to 2 meters or more behind image capture device 124and/or image capture device 126. This type of displacement may enableone of the image capture devices to cover potential blind spots of theother image capture device(s).

Image capture devices 122 may have any suitable resolution capability(e.g., number of pixels associated with the image sensor), and theresolution of the image sensor(s) associated with the image capturedevice 122 may be higher, lower, or the same as the resolution of theimage sensor(s) associated with image capture devices 124 and 126. Insome embodiments, the image sensor(s) associated with image capturedevice 122 and/or image capture devices 124 and 126 may have aresolution of 640×480, 1024×768, 1280×960, or any other suitableresolution.

The frame rate (e.g., the rate at which an image capture device acquiresa set of pixel data of one image frame before moving on to capture pixeldata associated with the next image frame) may be controllable. Theframe rate associated with image capture device 122 may be higher,lower, or the same as the frame rate associated with image capturedevices 124 and 126. The frame rate associated with image capturedevices 122, 124, and 126 may depend on a variety of factors that mayaffect the timing of the frame rate. For example, one or more of imagecapture devices 122, 124, and 126 may include a selectable pixel delayperiod imposed before or after acquisition of image data associated withone or more pixels of an image sensor in image capture device 122, 124,and/or 126. Generally, image data corresponding to each pixel may beacquired according to a clock rate for the device (e.g., one pixel perclock cycle). Additionally, in embodiments including a rolling shutter,one or more of image capture devices 122, 124, and 126 may include aselectable horizontal blanking period imposed before or afteracquisition of image data associated with a row of pixels of an imagesensor in image capture device 122, 124, and/or 126. Further, one ormore of image capture devices 122, 124, and/or 126 may include aselectable vertical blanking period imposed before or after acquisitionof image data associated with an image frame of image capture device122, 124, and 126.

These timing controls may enable synchronization of frame ratesassociated with image capture devices 122, 124, and 126, even where theline scan rates of each are different. Additionally, as will bediscussed in greater detail below, these selectable timing controls,among other factors (e.g., image sensor resolution, maximum line scanrates, etc.) may enable synchronization of image capture from an areawhere the FOV of image capture device 122 overlaps with one or more FOVsof image capture devices 124 and 126, even where the field of view ofimage capture device 122 is different from the FOVs of image capturedevices 124 and 126.

Frame rate timing in image capture device 122, 124, and 126 may dependon the resolution of the associated image sensors. For example, assumingsimilar line scan rates for both devices, if one device includes animage sensor having a resolution of 640×480 and another device includesan image sensor with a resolution of 1280×960, then more time will berequired to acquire a frame of image data from the sensor having thehigher resolution.

Another factor that may affect the timing of image data acquisition inimage capture devices 122, 124, and 126 is the maximum line scan rate.For example, acquisition of a row of image data from an image sensorincluded in image capture device 122, 124, and 126 will require someminimum amount of time. Assuming no pixel delay periods are added, thisminimum amount of time for acquisition of a row of image data will berelated to the maximum line scan rate for a particular device. Devicesthat offer higher maximum line scan rates have the potential to providehigher frame rates than devices with lower maximum line scan rates. Insome embodiments, one or more of image capture devices 124 and 126 mayhave a maximum line scan rate that is higher than a maximum line scanrate associated with image capture device 122. In some embodiments, themaximum line scan rate of image capture device 124 and/or 126 may be1.25, 1.5, 1.75, or 2 times or more than a maximum line scan rate ofimage capture device 122.

In another embodiment, image capture devices 122, 124, and 126 may havethe same maximum line scan rate, but image capture device 122 may beoperated at a scan rate less than or equal to its maximum scan rate. Thesystem may be configured such that one or more of image capture devices124 and 126 operate at a line scan rate that is equal to the line scanrate of image capture device 122. In other instances, the system may beconfigured such that the line scan rate of image capture device 124and/or image capture device 126 may be 1.25, 1.5, 1.75, or 2 times ormore than the line scan rate of image capture device 122.

In some embodiments, image capture devices 122, 124, and 126 may beasymmetric. That is, they may include cameras having different fields ofview (FOV) and focal lengths. The fields of view of image capturedevices 122, 124, and 126 may include any desired area relative to anenvironment of vehicle 200, for example. In some embodiments, one ormore of image capture devices 122, 124, and 126 may be configured toacquire image data from an environment in front of vehicle 200, behindvehicle 200, to the sides of vehicle 200, or combinations thereof.

Further, the focal length associated with each image capture device 122,124, and/or 126 may be selectable (e.g., by inclusion of appropriatelenses etc.) such that each device acquires images of objects at adesired distance range relative to vehicle 200. For example, in someembodiments image capture devices 122, 124, and 126 may acquire imagesof close-up objects within a few meters from the vehicle. Image capturedevices 122, 124, and 126 may also be configured to acquire images ofobjects at ranges more distant from the vehicle (e.g., 25 m, 50 m, 100m, 150 m, or more). Further, the focal lengths of image capture devices122, 124, and 126 may be selected such that one image capture device(e.g., image capture device 122) can acquire images of objectsrelatively close to the vehicle (e.g., within 10 m or within 20 m) whilethe other image capture devices (e.g., image capture devices 124 and126) can acquire images of more distant objects (e.g., greater than 20m, 50 m, 100 m, 150 m, etc.) from vehicle 200.

According to some embodiments, the FOV of one or more image capturedevices 122, 124, and 126 may have a wide angle. For example, it may beadvantageous to have a FOV of 140 degrees, especially for image capturedevices 122, 124, and 126 that may be used to capture images of the areain the vicinity of vehicle 200. For example, image capture device 122may be used to capture images of the area to the right or left ofvehicle 200 and, in such embodiments, it may be desirable for imagecapture device 122 to have a wide FOV (e.g., at least 140 degrees).

The field of view associated with each of image capture devices 122,124, and 126 may depend on the respective focal lengths. For example, asthe focal length increases, the corresponding field of view decreases.

Image capture devices 122, 124, and 126 may be configured to have anysuitable fields of view. In one particular example, image capture device122 may have a horizontal FOV of 46 degrees, image capture device 124may have a horizontal FOV of 23 degrees, and image capture device 126may have a horizontal FOV in between 23 and 46 degrees. In anotherinstance, image capture device 122 may have a horizontal FOV of 52degrees, image capture device 124 may have a horizontal FOV of 26degrees, and image capture device 126 may have a horizontal FOV inbetween 26 and 52 degrees. In some embodiments, a ratio of the FOV ofimage capture device 122 to the FOVs of image capture device 124 and/orimage capture device 126 may vary from 1.5 to 2.0. In other embodiments,this ratio may vary between 1.25 and 2.25.

System 100 may be configured so that a field of view of image capturedevice 122 overlaps, at least partially or fully, with a field of viewof image capture device 124 and/or image capture device 126. In someembodiments, system 100 may be configured such that the fields of viewof image capture devices 124 and 126, for example, fall within (e.g.,are narrower than) and share a common center with the field of view ofimage capture device 122. In other embodiments, the image capturedevices 122, 124, and 126 may capture adjacent FOVs or may have partialoverlap in their FOVs. In some embodiments, the fields of view of imagecapture devices 122, 124, and 126 may be aligned such that a center ofthe narrower FOV image capture devices 124 and/or 126 may be located ina lower half of the field of view of the wider FOV device 122.

FIG. 2F is a diagrammatic representation of exemplary vehicle controlsystems, consistent with the disclosed embodiments. As indicated in FIG.2F, vehicle 200 may include throttling system 220, braking system 230,and steering system 240. System 100 may provide inputs (e.g., controlsignals) to one or more of throttling system 220, braking system 230,and steering system 240 over one or more data links (e.g., any wiredand/or wireless link or links for transmitting data). For example, basedon analysis of images acquired by image capture devices 122, 124, and/or126, system 100 may provide control signals to one or more of throttlingsystem 220, braking system 230, and steering system 240 to navigatevehicle 200 (e.g., by causing an acceleration, a turn, a lane shift,etc.). Further, system 100 may receive inputs from one or more ofthrottling system 220, braking system 230, and steering system 24indicating operating conditions of vehicle 200 (e.g., speed, whethervehicle 200 is braking and/or turning, etc.). Further details areprovided in connection with FIGS. 4-7, below.

As shown in FIG. 3A, vehicle 200 may also include a user interface 170for interacting with a driver or a passenger of vehicle 200. Forexample, user interface 170 in a vehicle application may include a touchscreen 320, knobs 330, buttons 340, and a microphone 350. A driver orpassenger of vehicle 200 may also use handles (e.g., located on or nearthe steering column of vehicle 200 including, for example, turn signalhandles), buttons (e.g., located on the steering wheel of vehicle 200),and the like, to interact with system 100. In some embodiments,microphone 350 may be positioned adjacent to a rearview mirror 310.Similarly, in some embodiments, image capture device 122 may be locatednear rearview mirror 310. In some embodiments, user interface 170 mayalso include one or more speakers 360 (e.g., speakers of a vehicle audiosystem). For example, system 100 may provide various notifications(e.g., alerts) via speakers 360.

FIGS. 3B-3D are illustrations of an exemplary camera mount 370configured to be positioned behind a rearview mirror (e.g., rearviewmirror 310) and against a vehicle windshield, consistent with disclosedembodiments. As shown in FIG. 3B, camera mount 370 may include imagecapture devices 122, 124, and 126. Image capture devices 124 and 126 maybe positioned behind a glare shield 380, which may be flush against thevehicle windshield and include a composition of film and/oranti-reflective materials. For example, glare shield 380 may bepositioned such that the shield aligns against a vehicle windshieldhaving a matching slope. In some embodiments, each of image capturedevices 122, 124, and 126 may be positioned behind glare shield 380, asdepicted, for example, in FIG. 3D. The disclosed embodiments are notlimited to any particular configuration of image capture devices 122,124, and 126, camera mount 370, and glare shield 380. FIG. 3C is anillustration of camera mount 370 shown in FIG. 3B from a frontperspective.

As will be appreciated by a person skilled in the art having the benefitof this disclosure, numerous variations and/or modifications may be madeto the foregoing disclosed embodiments. For example, not all componentsare essential for the operation of system 100. Further, any componentmay be located in any appropriate part of system 100 and the componentsmay be rearranged into a variety of configurations while providing thefunctionality of the disclosed embodiments. Therefore, the foregoingconfigurations are examples and, regardless of the configurationsdiscussed above, system 100 can provide a wide range of functionality toanalyze the surroundings of vehicle 200 and navigate vehicle 200 inresponse to the analysis.

As discussed below in further detail and consistent with variousdisclosed embodiments, system 100 may provide a variety of featuresrelated to autonomous driving and/or driver assist technology. Forexample, system 100 may analyze image data, position data (e.g., GPSlocation information), map data, speed data, and/or data from sensorsincluded in vehicle 200. System 100 may collect the data for analysisfrom, for example, image acquisition unit 120, position sensor 130, andother sensors. Further, system 100 may analyze the collected data todetermine whether or not vehicle 200 should take a certain action, andthen automatically take the determined action without humanintervention. For example, when vehicle 200 navigates without humanintervention, system 100 may automatically control the braking,acceleration, and/or steering of vehicle 200 (e.g., by sending controlsignals to one or more of throttling system 220, braking system 230, andsteering system 240). Further, system 100 may analyze the collected dataand issue warnings and/or alerts to vehicle occupants based on theanalysis of the collected data. Additional details regarding the variousembodiments that are provided by system 100 are provided below.

Forward-Facing Multi-Imaging System

As discussed above, system 100 may provide drive assist functionalitythat uses a multi-camera system. The multi-camera system may use one ormore cameras facing in the forward direction of a vehicle. In otherembodiments, the multi-camera system may include one or more camerasfacing to the side of a vehicle or to the rear of the vehicle. In oneembodiment, for example, system 100 may use a two-camera imaging system,where a first camera and a second camera (e.g., image capture devices122 and 124) may be positioned at the front and/or the sides of avehicle (e.g., vehicle 200). The first camera may have a field of viewthat is greater than, less than, or partially overlapping with, thefield of view of the second camera. In addition, the first camera may beconnected to a first image processor to perform monocular image analysisof images provided by the first camera, and the second camera may beconnected to a second image processor to perform monocular imageanalysis of images provided by the second camera. The outputs (e.g.,processed information) of the first and second image processors may becombined. In some embodiments, the second image processor may receiveimages from both the first camera and second camera to perform stereoanalysis. In another embodiment, system 100 may use a three-cameraimaging system where each of the cameras has a different field of view.Such a system may, therefore, make decisions based on informationderived from objects located at varying distances both forward and tothe sides of the vehicle. References to monocular image analysis mayrefer to instances where image analysis is performed based on imagescaptured from a single point of view (e.g., from a single camera).Stereo image analysis may refer to instances where image analysis isperformed based on two or more images captured with one or morevariations of an image capture parameter. For example, captured imagessuitable for performing stereo image analysis may include imagescaptured: from two or more different positions, from different fields ofview, using different focal lengths, along with parallax information,etc.

For example, in one embodiment, system 100 may implement a three cameraconfiguration using image capture devices 122, 124, and 126. In such aconfiguration, image capture device 122 may provide a narrow field ofview (e.g., 34 degrees, or other values selected from a range of about20 to 45 degrees, etc.), image capture device 124 may provide a widefield of view (e.g., 150 degrees or other values selected from a rangeof about 100 to about 180 degrees), and image capture device 126 mayprovide an intermediate field of view (e.g., 46 degrees or other valuesselected from a range of about 35 to about 60 degrees). In someembodiments, image capture device 126 may act as a main or primarycamera. Image capture devices 122, 124, and 126 may be positioned behindrearview mirror 310 and positioned substantially side-by-side (e.g., 6cm apart). Further, in some embodiments, as discussed above, one or moreof image capture devices 122, 124, and 126 may be mounted behind glareshield 380 that is flush with the windshield of vehicle 200. Suchshielding may act to minimize the impact of any reflections from insidethe car on image capture devices 122, 124, and 126.

In another embodiment, as discussed above in connection with FIGS. 3Band 3C, the wide field of view camera (e.g., image capture device 124 inthe above example) may be mounted lower than the narrow and main fieldof view cameras (e.g., image devices 122 and 126 in the above example).This configuration may provide a free line of sight from the wide fieldof view camera. To reduce reflections, the cameras may be mounted closeto the windshield of vehicle 200, and may include polarizers on thecameras to damp reflected light.

A three camera system may provide certain performance characteristics.For example, some embodiments may include an ability to validate thedetection of objects by one camera based on detection results fromanother camera. In the three camera configuration discussed above,processing unit 110 may include, for example, three processing devices(e.g., three EyeQ series of processor chips, as discussed above), witheach processing device dedicated to processing images captured by one ormore of image capture devices 122, 124, and 126.

In a three camera system, a first processing device may receive imagesfrom both the main camera and the narrow field of view camera, andperform vision processing of the narrow FOV camera to, for example,detect other vehicles, pedestrians, lane marks, traffic signs, trafficlights, and other road objects. Further, the first processing device maycalculate a disparity of pixels between the images from the main cameraand the narrow camera and create a 3D reconstruction of the environmentof vehicle 200. The first processing device may then combine the 3Dreconstruction with 3D map data or with 3D information calculated basedon information from another camera.

The second processing device may receive images from main camera andperform vision processing to detect other vehicles, pedestrians, lanemarks, traffic signs, traffic lights, and other road objects.Additionally, the second processing device may calculate a cameradisplacement and, based on the displacement, calculate a disparity ofpixels between successive images and create a 3D reconstruction of thescene (e.g., a structure from motion). The second processing device maysend the structure from motion based 3D reconstruction to the firstprocessing device to be combined with the stereo 3D images.

The third processing device may receive images from the wide FOV cameraand process the images to detect vehicles, pedestrians, lane marks,traffic signs, traffic lights, and other road objects. The thirdprocessing device may further execute additional processing instructionsto analyze images to identify objects moving in the image, such asvehicles changing lanes, pedestrians, etc.

In some embodiments, having streams of image-based information capturedand processed independently may provide an opportunity for providingredundancy in the system. Such redundancy may include, for example,using a first image capture device and the images processed from thatdevice to validate and/or supplement information obtained by capturingand processing image information from at least a second image capturedevice.

In some embodiments, system 100 may use two image capture devices (e.g.,image capture devices 122 and 124) in providing navigation assistancefor vehicle 200 and use a third image capture device (e.g., imagecapture device 126) to provide redundancy and validate the analysis ofdata received from the other two image capture devices. For example, insuch a configuration, image capture devices 122 and 124 may provideimages for stereo analysis by system 100 for navigating vehicle 200,while image capture device 126 may provide images for monocular analysisby system 100 to provide redundancy and validation of informationobtained based on images captured from image capture device 122 and/orimage capture device 124. That is, image capture device 126 (and acorresponding processing device) may be considered to provide aredundant sub-system for providing a check on the analysis derived fromimage capture devices 122 and 124 (e.g., to provide an automaticemergency braking (AEB) system). Furthermore, in some embodiments,redundancy and validation of received data may be supplemented based oninformation received from one more sensors (e.g., radar, lidar, acousticsensors, information received from one or more transceivers outside of avehicle, etc.).

One of skill in the art will recognize that the above cameraconfigurations, camera placements, number of cameras, camera locations,etc., are examples only. These components and others described relativeto the overall system may be assembled and used in a variety ofdifferent configurations without departing from the scope of thedisclosed embodiments. Further details regarding usage of a multi-camerasystem to provide driver assist and/or autonomous vehicle functionalityfollow below.

FIG. 4 is an exemplary functional block diagram of memory 140 and/or150, which may be stored/programmed with instructions for performing oneor more operations consistent with the disclosed embodiments. Althoughthe following refers to memory 140, one of skill in the art willrecognize that instructions may be stored in memory 140 and/or 150.

As shown in FIG. 4, memory 140 may store a monocular image analysismodule 402, a stereo image analysis module 404, a velocity andacceleration module 406, and a navigational response module 408. Thedisclosed embodiments are not limited to any particular configuration ofmemory 140.

Further, application processor 180 and/or image processor 190 mayexecute the instructions stored in any of modules 402, 404, 406, and 408included in memory 140. One of skill in the art will understand thatreferences in the following discussions to processing unit 110 may referto application processor 180 and image processor 190 individually orcollectively. Accordingly, steps of any of the following processes maybe performed by one or more processing devices.

In one embodiment, monocular image analysis module 402 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs monocular image analysis of a set ofimages acquired by one of image capture devices 122, 124, and 126. Insome embodiments, processing unit 110 may combine information from a setof images with additional sensory information (e.g., information fromradar, lidar, etc.) to perform the monocular image analysis. Asdescribed in connection with FIGS. 5A-5D below, monocular image analysismodule 402 may include instructions for detecting a set of featureswithin the set of images, such as lane markings, vehicles, pedestrians,road signs, highway exit ramps, traffic lights, hazardous objects, andany other feature associated with an environment of a vehicle. Based onthe analysis, system 100 (e.g., via processing unit 110) may cause oneor more navigational responses in vehicle 200, such as a turn, a laneshift, a change in acceleration, and the like, as discussed below inconnection with navigational response module 408.

In one embodiment, stereo image analysis module 404 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs stereo image analysis of first and secondsets of images acquired by a combination of image capture devicesselected from any of image capture devices 122, 124, and 126. In someembodiments, processing unit 110 may combine information from the firstand second sets of images with additional sensory information (e.g.,information from radar) to perform the stereo image analysis. Forexample, stereo image analysis module 404 may include instructions forperforming stereo image analysis based on a first set of images acquiredby image capture device 124 and a second set of images acquired by imagecapture device 126. As described in connection with FIG. 6 below, stereoimage analysis module 404 may include instructions for detecting a setof features within the first and second sets of images, such as lanemarkings, vehicles, pedestrians, road signs, highway exit ramps, trafficlights, hazardous objects, and the like. Based on the analysis,processing unit 110 may cause one or more navigational responses invehicle 200, such as a turn, a lane shift, a change in acceleration, andthe like, as discussed below in connection with navigational responsemodule 408. Furthermore, in some embodiments, stereo image analysismodule 404 may implement techniques associated with a trained system(such as a neural network or a deep neural network) or an untrainedsystem, such as a system that may be configured to use computer visionalgorithms to detect and/or label objects in an environment from whichsensory information was captured and processed. In one embodiment,stereo image analysis module 404 and/or other image processing modulesmay be configured to use a combination of a trained and untrainedsystem.

In one embodiment, velocity and acceleration module 406 may storesoftware configured to analyze data received from one or more computingand electromechanical devices in vehicle 200 that are configured tocause a change in velocity and/or acceleration of vehicle 200. Forexample, processing unit 110 may execute instructions associated withvelocity and acceleration module 406 to calculate a target speed forvehicle 200 based on data derived from execution of monocular imageanalysis module 402 and/or stereo image analysis module 404. Such datamay include, for example, a target position, velocity, and/oracceleration, the position and/or speed of vehicle 200 relative to anearby vehicle, pedestrian, or road object, position information forvehicle 200 relative to lane markings of the road, and the like. Inaddition, processing unit 110 may calculate a target speed for vehicle200 based on sensory input (e.g., information from radar) and input fromother systems of vehicle 200, such as throttling system 220, brakingsystem 230, and/or steering system 240 of vehicle 200. Based on thecalculated target speed, processing unit 110 may transmit electronicsignals to throttling system 220, braking system 230, and/or steeringsystem 240 of vehicle 200 to trigger a change in velocity and/oracceleration by, for example, physically depressing the brake or easingup off the accelerator of vehicle 200.

In one embodiment, navigational response module 408 may store softwareexecutable by processing unit 110 to determine a desired navigationalresponse based on data derived from execution of monocular imageanalysis module 402 and/or stereo image analysis module 404. Such datamay include position and speed information associated with nearbyvehicles, pedestrians, and road objects, target position information forvehicle 200, and the like. Additionally, in some embodiments, thenavigational response may be based (partially or fully) on map data, apredetermined position of vehicle 200, and/or a relative velocity or arelative acceleration between vehicle 200 and one or more objectsdetected from execution of monocular image analysis module 402 and/orstereo image analysis module 404. Navigational response module 408 mayalso determine a desired navigational response based on sensory input(e.g., information from radar) and inputs from other systems of vehicle200, such as throttling system 220, braking system 230, and steeringsystem 240 of vehicle 200. Based on the desired navigational response,processing unit 110 may transmit electronic signals to throttling system220, braking system 230, and steering system 240 of vehicle 200 totrigger a desired navigational response by, for example, turning thesteering wheel of vehicle 200 to achieve a rotation of a predeterminedangle. In some embodiments, processing unit 110 may use the output ofnavigational response module 408 (e.g., the desired navigationalresponse) as an input to execution of velocity and acceleration module406 for calculating a change in speed of vehicle 200.

Furthermore, any of the modules (e.g., modules 402, 404, and 406)disclosed herein may implement techniques associated with a trainedsystem (such as a neural network or a deep neural network) or anuntrained system.

FIG. 5A is a flowchart showing an exemplary process 500A for causing oneor more navigational responses based on monocular image analysis,consistent with disclosed embodiments. At step 510, processing unit 110may receive a plurality of images via data interface 128 betweenprocessing unit 110 and image acquisition unit 120. For instance, acamera included in image acquisition unit 120 (such as image capturedevice 122 having field of view 202) may capture a plurality of imagesof an area forward of vehicle 200 (or to the sides or rear of a vehicle,for example) and transmit them over a data connection (e.g., digital,wired, USB, wireless, Bluetooth, etc.) to processing unit 110.Processing unit 110 may execute monocular image analysis module 402 toanalyze the plurality of images at step 520, as described in furtherdetail in connection with FIGS. 5B-5D below. By performing the analysis,processing unit 110 may detect a set of features within the set ofimages, such as lane markings, vehicles, pedestrians, road signs,highway exit ramps, traffic lights, and the like.

Processing unit 110 may also execute monocular image analysis module 402to detect various road hazards at step 520, such as, for example, partsof a truck tire, fallen road signs, loose cargo, small animals, and thelike. Road hazards may vary in structure, shape, size, and color, whichmay make detection of such hazards more challenging. In someembodiments, processing unit 110 may execute monocular image analysismodule 402 to perform multi-frame analysis on the plurality of images todetect road hazards. For example, processing unit 110 may estimatecamera motion between consecutive image frames and calculate thedisparities in pixels between the frames to construct a 3D-map of theroad. Processing unit 110 may then use the 3D-map to detect the roadsurface, as well as hazards existing above the road surface.

At step 530, processing unit 110 may execute navigational responsemodule 408 to cause one or more navigational responses in vehicle 200based on the analysis performed at step 520 and the techniques asdescribed above in connection with FIG. 4. Navigational responses mayinclude, for example, a turn, a lane shift, a change in acceleration,and the like. In some embodiments, processing unit 110 may use dataderived from execution of velocity and acceleration module 406 to causethe one or more navigational responses. Additionally, multiplenavigational responses may occur simultaneously, in sequence, or anycombination thereof. For instance, processing unit 110 may cause vehicle200 to shift one lane over and then accelerate by, for example,sequentially transmitting control signals to steering system 240 andthrottling system 220 of vehicle 200. Alternatively, processing unit 110may cause vehicle 200 to brake while at the same time shifting lanes by,for example, simultaneously transmitting control signals to brakingsystem 230 and steering system 240 of vehicle 200.

FIG. 5B is a flowchart showing an exemplary process 500B for detectingone or more vehicles and/or pedestrians in a set of images, consistentwith disclosed embodiments. Processing unit 110 may execute monocularimage analysis module 402 to implement process 500B. At step 540,processing unit 110 may determine a set of candidate objectsrepresenting possible vehicles and/or pedestrians. For example,processing unit 110 may scan one or more images, compare the images toone or more predetermined patterns, and identify within each imagepossible locations that may contain objects of interest (e.g., vehicles,pedestrians, or portions thereof). The predetermined patterns may bedesigned in such a way to achieve a high rate of “false hits” and a lowrate of “misses.” For example, processing unit 110 may use a lowthreshold of similarity to predetermined patterns for identifyingcandidate objects as possible vehicles or pedestrians. Doing so mayallow processing unit 110 to reduce the probability of missing (e.g.,not identifying) a candidate object representing a vehicle orpedestrian.

At step 542, processing unit 110 may filter the set of candidate objectsto exclude certain candidates (e.g., irrelevant or less relevantobjects) based on classification criteria. Such criteria may be derivedfrom various properties associated with object types stored in adatabase (e.g., a database stored in memory 140). Properties may includeobject shape, dimensions, texture, position (e.g., relative to vehicle200), and the like. Thus, processing unit 110 may use one or more setsof criteria to reject false candidates from the set of candidateobjects.

At step 544, processing unit 110 may analyze multiple frames of imagesto determine whether objects in the set of candidate objects representvehicles and/or pedestrians. For example, processing unit 110 may tracka detected candidate object across consecutive frames and accumulateframe-by-frame data associated with the detected object (e.g., size,position relative to vehicle 200, etc.). Additionally, processing unit110 may estimate parameters for the detected object and compare theobject's frame-by-frame position data to a predicted position.

At step 546, processing unit 110 may construct a set of measurements forthe detected objects. Such measurements may include, for example,position, velocity, and acceleration values (relative to vehicle 200)associated with the detected objects. In some embodiments, processingunit 110 may construct the measurements based on estimation techniquesusing a series of time-based observations such as Kalman filters orlinear quadratic estimation (LQE), and/or based on available modelingdata for different object types (e.g., cars, trucks, pedestrians,bicycles, road signs, etc.). The Kalman filters may be based on ameasurement of an object's scale, where the scale measurement isproportional to a time to collision (e.g., the amount of time forvehicle 200 to reach the object). Thus, by performing steps 540-546,processing unit 110 may identify vehicles and pedestrians appearingwithin the set of captured images and derive information (e.g.,position, speed, size) associated with the vehicles and pedestrians.Based on the identification and the derived information, processing unit110 may cause one or more navigational responses in vehicle 200, asdescribed in connection with FIG. 5A, above.

At step 548, processing unit 110 may perform an optical flow analysis ofone or more images to reduce the probabilities of detecting a “falsehit” and missing a candidate object that represents a vehicle orpedestrian. The optical flow analysis may refer to, for example,analyzing motion patterns relative to vehicle 200 in the one or moreimages associated with other vehicles and pedestrians, and that aredistinct from road surface motion. Processing unit 110 may calculate themotion of candidate objects by observing the different positions of theobjects across multiple image frames, which are captured at differenttimes. Processing unit 110 may use the position and time values asinputs into mathematical models for calculating the motion of thecandidate objects. Thus, optical flow analysis may provide anothermethod of detecting vehicles and pedestrians that are nearby vehicle200. Processing unit 110 may perform optical flow analysis incombination with steps 540-546 to provide redundancy for detectingvehicles and pedestrians and increase the reliability of system 100.

FIG. 5C is a flowchart showing an exemplary process 500C for detectingroad marks and/or lane geometry information in a set of images,consistent with disclosed embodiments. Processing unit 110 may executemonocular image analysis module 402 to implement process 500C. At step550, processing unit 110 may detect a set of objects by scanning one ormore images. To detect segments of lane markings, lane geometryinformation, and other pertinent road marks, processing unit 110 mayfilter the set of objects to exclude those determined to be irrelevant(e.g., minor potholes, small rocks, etc.). At step 552, processing unit110 may group together the segments detected in step 550 belonging tothe same road mark or lane mark. Based on the grouping, processing unit110 may develop a model to represent the detected segments, such as amathematical model.

At step 554, processing unit 110 may construct a set of measurementsassociated with the detected segments. In some embodiments, processingunit 110 may create a projection of the detected segments from the imageplane onto the real-world plane. The projection may be characterizedusing a 3rd-degree polynomial having coefficients corresponding tophysical properties such as the position, slope, curvature, andcurvature derivative of the detected road. In generating the projection,processing unit 110 may take into account changes in the road surface,as well as pitch and roll rates associated with vehicle 200. Inaddition, processing unit 110 may model the road elevation by analyzingposition and motion cues present on the road surface. Further,processing unit 110 may estimate the pitch and roll rates associatedwith vehicle 200 by tracking a set of feature points in the one or moreimages.

At step 556, processing unit 110 may perform multi-frame analysis by,for example, tracking the detected segments across consecutive imageframes and accumulating frame-by-frame data associated with detectedsegments. As processing unit 110 performs multi-frame analysis, the setof measurements constructed at step 554 may become more reliable andassociated with an increasingly higher confidence level. Thus, byperforming steps 550, 552, 554, and 556, processing unit 110 mayidentify road marks appearing within the set of captured images andderive lane geometry information. Based on the identification and thederived information, processing unit 110 may cause one or morenavigational responses in vehicle 200, as described in connection withFIG. 5A, above.

At step 558, processing unit 110 may consider additional sources ofinformation to further develop a safety model for vehicle 200 in thecontext of its surroundings. Processing unit 110 may use the safetymodel to define a context in which system 100 may execute autonomouscontrol of vehicle 200 in a safe manner. To develop the safety model, insome embodiments, processing unit 110 may consider the position andmotion of other vehicles, the detected road edges and barriers, and/orgeneral road shape descriptions extracted from map data (such as datafrom map database 160). By considering additional sources ofinformation, processing unit 110 may provide redundancy for detectingroad marks and lane geometry and increase the reliability of system 100.

FIG. 5D is a flowchart showing an exemplary process 500D for detectingtraffic lights in a set of images, consistent with disclosedembodiments. Processing unit 110 may execute monocular image analysismodule 402 to implement process 500D. At step 560, processing unit 110may scan the set of images and identify objects appearing at locationsin the images likely to contain traffic lights. For example, processingunit 110 may filter the identified objects to construct a set ofcandidate objects, excluding those objects unlikely to correspond totraffic lights. The filtering may be done based on various propertiesassociated with traffic lights, such as shape, dimensions, texture,position (e.g., relative to vehicle 200), and the like. Such propertiesmay be based on multiple examples of traffic lights and traffic controlsignals and stored in a database. In some embodiments, processing unit110 may perform multi-frame analysis on the set of candidate objectsreflecting possible traffic lights. For example, processing unit 110 maytrack the candidate objects across consecutive image frames, estimatethe real-world position of the candidate objects, and filter out thoseobjects that are moving (which are unlikely to be traffic lights). Insome embodiments, processing unit 110 may perform color analysis on thecandidate objects and identify the relative position of the detectedcolors appearing inside possible traffic lights.

At step 562, processing unit 110 may analyze the geometry of a junction.The analysis may be based on any combination of: (i) the number of lanesdetected on either side of vehicle 200, (ii) markings (such as arrowmarks) detected on the road, and (iii) descriptions of the junctionextracted from map data (such as data from map database 160). Processingunit 110 may conduct the analysis using information derived fromexecution of monocular analysis module 402. In addition, Processing unit110 may determine a correspondence between the traffic lights detectedat step 560 and the lanes appearing near vehicle 200.

As vehicle 200 approaches the junction, at step 564, processing unit 110may update the confidence level associated with the analyzed junctiongeometry and the detected traffic lights. For instance, the number oftraffic lights estimated to appear at the junction as compared with thenumber actually appearing at the junction may impact the confidencelevel. Thus, based on the confidence level, processing unit 110 maydelegate control to the driver of vehicle 200 in order to improve safetyconditions. By performing steps 560, 562, and 564, processing unit 110may identify traffic lights appearing within the set of captured imagesand analyze junction geometry information. Based on the identificationand the analysis, processing unit 110 may cause one or more navigationalresponses in vehicle 200, as described in connection with FIG. 5A,above.

FIG. 5E is a flowchart showing an exemplary process 500E for causing oneor more navigational responses in vehicle 200 based on a vehicle path,consistent with the disclosed embodiments. At step 570, processing unit110 may construct an initial vehicle path associated with vehicle 200.The vehicle path may be represented using a set of points expressed incoordinates (x, z), and the distance d_(i) between two points in the setof points may fall in the range of 1 to 5 meters. In one embodiment,processing unit 110 may construct the initial vehicle path using twopolynomials, such as left and right road polynomials. Processing unit110 may calculate the geometric midpoint between the two polynomials andoffset each point included in the resultant vehicle path by apredetermined offset (e.g., a smart lane offset), if any (an offset ofzero may correspond to travel in the middle of a lane). The offset maybe in a direction perpendicular to a segment between any two points inthe vehicle path. In another embodiment, processing unit 110 may use onepolynomial and an estimated lane width to offset each point of thevehicle path by half the estimated lane width plus a predeterminedoffset (e.g., a smart lane offset).

At step 572, processing unit 110 may update the vehicle path constructedat step 570. Processing unit 110 may reconstruct the vehicle pathconstructed at step 570 using a higher resolution, such that thedistance d_(k) between two points in the set of points representing thevehicle path is less than the distance d_(i) described above. Forexample, the distance d_(k) may fall in the range of 0.1 to 0.3 meters.Processing unit 110 may reconstruct the vehicle path using a parabolicspline algorithm, which may yield a cumulative distance vector Scorresponding to the total length of the vehicle path (i.e., based onthe set of points representing the vehicle path).

At step 574, processing unit 110 may determine a look-ahead point(expressed in coordinates as (x_(i), z_(i))) based on the updatedvehicle path constructed at step 572. Processing unit 110 may extractthe look-ahead point from the cumulative distance vector S, and thelook-ahead point may be associated with a look-ahead distance andlook-ahead time. The look-ahead distance, which may have a lower boundranging from 10 to 20 meters, may be calculated as the product of thespeed of vehicle 200 and the look-ahead time. For example, as the speedof vehicle 200 decreases, the look-ahead distance may also decrease(e.g., until it reaches the lower bound). The look-ahead time, which mayrange from 0.5 to 1.5 seconds, may be inversely proportional to the gainof one or more control loops associated with causing a navigationalresponse in vehicle 200, such as the heading error tracking controlloop. For example, the gain of the heading error tracking control loopmay depend on the bandwidth of a yaw rate loop, a steering actuatorloop, car lateral dynamics, and the like. Thus, the higher the gain ofthe heading error tracking control loop, the lower the look-ahead time.

At step 576, processing unit 110 may determine a heading error and yawrate command based on the look-ahead point determined at step 574.Processing unit 110 may determine the heading error by calculating thearctangent of the look-ahead point, e.g., arctan (x_(l)/z_(l)).Processing unit 110 may determine the yaw rate command as the product ofthe heading error and a high-level control gain. The high-level controlgain may be equal to: (2/look-ahead time), if the look-ahead distance isnot at the lower bound. Otherwise, the high-level control gain may beequal to: (2*speed of vehicle 200/look-ahead distance).

FIG. 5F is a flowchart showing an exemplary process 500F for determiningwhether a leading vehicle is changing lanes, consistent with thedisclosed embodiments. At step 580, processing unit 110 may determinenavigation information associated with a leading vehicle (e.g., avehicle traveling ahead of vehicle 200). For example, processing unit110 may determine the position, velocity (e.g., direction and speed),and/or acceleration of the leading vehicle, using the techniquesdescribed in connection with FIGS. 5A and 5B, above. Processing unit 110may also determine one or more road polynomials, a look-ahead point(associated with vehicle 200), and/or a snail trail (e.g., a set ofpoints describing a path taken by the leading vehicle), using thetechniques described in connection with FIG. 5E, above.

At step 582, processing unit 110 may analyze the navigation informationdetermined at step 580. In one embodiment, processing unit 110 maycalculate the distance between a snail trail and a road polynomial(e.g., along the trail). If the variance of this distance along thetrail exceeds a predetermined threshold (for example, 0.1 to 0.2 meterson a straight road, 0.3 to 0.4 meters on a moderately curvy road, and0.5 to 0.6 meters on a road with sharp curves), processing unit 110 maydetermine that the leading vehicle is likely changing lanes. In the casewhere multiple vehicles are detected traveling ahead of vehicle 200,processing unit 110 may compare the snail trails associated with eachvehicle. Based on the comparison, processing unit 110 may determine thata vehicle whose snail trail does not match with the snail trails of theother vehicles is likely changing lanes. Processing unit 110 mayadditionally compare the curvature of the snail trail (associated withthe leading vehicle) with the expected curvature of the road segment inwhich the leading vehicle is traveling. The expected curvature may beextracted from map data (e.g., data from map database 160), from roadpolynomials, from other vehicles' snail trails, from prior knowledgeabout the road, and the like. If the difference in curvature of thesnail trail and the expected curvature of the road segment exceeds apredetermined threshold, processing unit 110 may determine that theleading vehicle is likely changing lanes.

In another embodiment, processing unit 110 may compare the leadingvehicle's instantaneous position with the look-ahead point (associatedwith vehicle 200) over a specific period of time (e.g., 0.5 to 1.5seconds). If the distance between the leading vehicle's instantaneousposition and the look-ahead point varies during the specific period oftime, and the cumulative sum of variation exceeds a predeterminedthreshold (for example, 0.3 to 0.4 meters on a straight road, 0.7 to 0.8meters on a moderately curvy road, and 1.3 to 1.7 meters on a road withsharp curves), processing unit 110 may determine that the leadingvehicle is likely changing lanes. In another embodiment, processing unit110 may analyze the geometry of the snail trail by comparing the lateraldistance traveled along the trail with the expected curvature of thesnail trail. The expected radius of curvature may be determinedaccording to the calculation: (δ_(z) ²+δ_(x) ²)/2/(δ_(x)), where δ_(x)represents the lateral distance traveled and δ_(z) represents thelongitudinal distance traveled. If the difference between the lateraldistance traveled and the expected curvature exceeds a predeterminedthreshold (e.g., 500 to 700 meters), processing unit 110 may determinethat the leading vehicle is likely changing lanes. In anotherembodiment, processing unit 110 may analyze the position of the leadingvehicle. If the position of the leading vehicle obscures a roadpolynomial (e.g., the leading vehicle is overlaid on top of the roadpolynomial), then processing unit 110 may determine that the leadingvehicle is likely changing lanes. In the case where the position of theleading vehicle is such that, another vehicle is detected ahead of theleading vehicle and the snail trails of the two vehicles are notparallel, processing unit 110 may determine that the (closer) leadingvehicle is likely changing lanes.

At step 584, processing unit 110 may determine whether or not leadingvehicle 200 is changing lanes based on the analysis performed at step582. For example, processing unit 110 may make the determination basedon a weighted average of the individual analyses performed at step 582.Under such a scheme, for example, a decision by processing unit 110 thatthe leading vehicle is likely changing lanes based on a particular typeof analysis may be assigned a value of “1” (and “0” to represent adetermination that the leading vehicle is not likely changing lanes).Different analyses performed at step 582 may be assigned differentweights, and the disclosed embodiments are not limited to any particularcombination of analyses and weights.

FIG. 6 is a flowchart showing an exemplary process 600 for causing oneor more navigational responses based on stereo image analysis,consistent with disclosed embodiments. At step 610, processing unit 110may receive a first and second plurality of images via data interface128. For example, cameras included in image acquisition unit 120 (suchas image capture devices 122 and 124 having fields of view 202 and 204)may capture a first and second plurality of images of an area forward ofvehicle 200 and transmit them over a digital connection (e.g., USB,wireless, Bluetooth, etc.) to processing unit 110. In some embodiments,processing unit 110 may receive the first and second plurality of imagesvia two or more data interfaces. The disclosed embodiments are notlimited to any particular data interface configurations or protocols.

At step 620, processing unit 110 may execute stereo image analysismodule 404 to perform stereo image analysis of the first and secondplurality of images to create a 3D map of the road in front of thevehicle and detect features within the images, such as lane markings,vehicles, pedestrians, road signs, highway exit ramps, traffic lights,road hazards, and the like. Stereo image analysis may be performed in amanner similar to the steps described in connection with FIGS. 5A-5D,above. For example, processing unit 110 may execute stereo imageanalysis module 404 to detect candidate objects (e.g., vehicles,pedestrians, road marks, traffic lights, road hazards, etc.) within thefirst and second plurality of images, filter out a subset of thecandidate objects based on various criteria, and perform multi-frameanalysis, construct measurements, and determine a confidence level forthe remaining candidate objects. In performing the steps above,processing unit 110 may consider information from both the first andsecond plurality of images, rather than information from one set ofimages alone. For example, processing unit 110 may analyze thedifferences in pixel-level data (or other data subsets from among thetwo streams of captured images) for a candidate object appearing in boththe first and second plurality of images. As another example, processingunit 110 may estimate a position and/or velocity of a candidate object(e.g., relative to vehicle 200) by observing that the object appears inone of the plurality of images but not the other or relative to otherdifferences that may exist relative to objects appearing if the twoimage streams. For example, position, velocity, and/or accelerationrelative to vehicle 200 may be determined based on trajectories,positions, movement characteristics, etc. of features associated with anobject appearing in one or both of the image streams.

At step 630, processing unit 110 may execute navigational responsemodule 408 to cause one or more navigational responses in vehicle 200based on the analysis performed at step 620 and the techniques asdescribed above in connection with FIG. 4. Navigational responses mayinclude, for example, a turn, a lane shift, a change in acceleration, achange in velocity, braking, and the like. In some embodiments,processing unit 110 may use data derived from execution of velocity andacceleration module 406 to cause the one or more navigational responses.Additionally, multiple navigational responses may occur simultaneously,in sequence, or any combination thereof.

FIG. 7 is a flowchart showing an exemplary process 700 for causing oneor more navigational responses based on an analysis of three sets ofimages, consistent with disclosed embodiments. At step 710, processingunit 110 may receive a first, second, and third plurality of images viadata interface 128. For instance, cameras included in image acquisitionunit 120 (such as image capture devices 122, 124, and 126 having fieldsof view 202, 204, and 206) may capture a first, second, and thirdplurality of images of an area forward and/or to the side of vehicle 200and transmit them over a digital connection (e.g., USB, wireless,Bluetooth, etc.) to processing unit 110. In some embodiments, processingunit 110 may receive the first, second, and third plurality of imagesvia three or more data interfaces. For example, each of image capturedevices 122, 124, 126 may have an associated data interface forcommunicating data to processing unit 110. The disclosed embodiments arenot limited to any particular data interface configurations orprotocols.

At step 720, processing unit 110 may analyze the first, second, andthird plurality of images to detect features within the images, such aslane markings, vehicles, pedestrians, road signs, highway exit ramps,traffic lights, road hazards, and the like. The analysis may beperformed in a manner similar to the steps described in connection withFIGS. 5A-5D and 6, above. For instance, processing unit 110 may performmonocular image analysis (e.g., via execution of monocular imageanalysis module 402 and based on the steps described in connection withFIGS. 5A-5D, above) on each of the first, second, and third plurality ofimages. Alternatively, processing unit 110 may perform stereo imageanalysis (e.g., via execution of stereo image analysis module 404 andbased on the steps described in connection with FIG. 6, above) on thefirst and second plurality of images, the second and third plurality ofimages, and/or the first and third plurality of images. The processedinformation corresponding to the analysis of the first, second, and/orthird plurality of images may be combined. In some embodiments,processing unit 110 may perform a combination of monocular and stereoimage analyses. For example, processing unit 110 may perform monocularimage analysis (e.g., via execution of monocular image analysis module402) on the first plurality of images and stereo image analysis (e.g.,via execution of stereo image analysis module 404) on the second andthird plurality of images. The configuration of image capture devices122, 124, and 126—including their respective locations and fields ofview 202, 204, and 206—may influence the types of analyses conducted onthe first, second, and third plurality of images. The disclosedembodiments are not limited to a particular configuration of imagecapture devices 122, 124, and 126, or the types of analyses conducted onthe first, second, and third plurality of images.

In some embodiments, processing unit 110 may perform testing on system100 based on the images acquired and analyzed at steps 710 and 720. Suchtesting may provide an indicator of the overall performance of system100 for certain configurations of image capture devices 122, 124, and126. For example, processing unit 110 may determine the proportion of“false hits” (e.g., cases where system 100 incorrectly determined thepresence of a vehicle or pedestrian) and “misses.”

At step 730, processing unit 110 may cause one or more navigationalresponses in vehicle 200 based on information derived from two of thefirst, second, and third plurality of images. Selection of two of thefirst, second, and third plurality of images may depend on variousfactors, such as, for example, the number, types, and sizes of objectsdetected in each of the plurality of images. Processing unit 110 mayalso make the selection based on image quality and resolution, theeffective field of view reflected in the images, the number of capturedframes, the extent to which one or more objects of interest actuallyappear in the frames (e.g., the percentage of frames in which an objectappears, the proportion of the object that appears in each such frame,etc.), and the like.

In some embodiments, processing unit 110 may select information derivedfrom two of the first, second, and third plurality of images bydetermining the extent to which information derived from one imagesource is consistent with information derived from other image sources.For example, processing unit 110 may combine the processed informationderived from each of image capture devices 122, 124, and 126 (whether bymonocular analysis, stereo analysis, or any combination of the two) anddetermine visual indicators (e.g., lane markings, a detected vehicle andits location and/or path, a detected traffic light, etc.) that areconsistent across the images captured from each of image capture devices122, 124, and 126. Processing unit 110 may also exclude information thatis inconsistent across the captured images (e.g., a vehicle changinglanes, a lane model indicating a vehicle that is too close to vehicle200, etc.). Thus, processing unit 110 may select information derivedfrom two of the first, second, and third plurality of images based onthe determinations of consistent and inconsistent information.

Navigational responses may include, for example, a turn, a lane shift, achange in acceleration, and the like. Processing unit 110 may cause theone or more navigational responses based on the analysis performed atstep 720 and the techniques as described above in connection with FIG.4. Processing unit 110 may also use data derived from execution ofvelocity and acceleration module 406 to cause the one or morenavigational responses. In some embodiments, processing unit 110 maycause the one or more navigational responses based on a relativeposition, relative velocity, and/or relative acceleration betweenvehicle 200 and an object detected within any of the first, second, andthird plurality of images. Multiple navigational responses may occursimultaneously, in sequence, or any combination thereof

Sparse Road Model for Autonomous Vehicle Navigation

In some embodiments, the disclosed systems and methods may use a sparsemap for autonomous vehicle navigation. In particular, the sparse map maybe for autonomous vehicle navigation along a road segment. For example,the sparse map may provide sufficient information for navigating anautonomous vehicle without storing and/or updating a large quantity ofdata. As discussed below in further detail, an autonomous vehicle mayuse the sparse map to navigate one or more roads based on one or morestored trajectories.

Sparse Map for Autonomous Vehicle Navigation

In some embodiments, the disclosed systems and methods may generate asparse map for autonomous vehicle navigation. For example, the sparsemap may provide sufficient information for navigation without requiringexcessive data storage or data transfer rates. As discussed below infurther detail, a vehicle (which may be an autonomous vehicle) may usethe sparse map to navigate one or more roads. For example, in someembodiments, the sparse map may include data related to a road andpotentially landmarks along the road that may be sufficient for vehiclenavigation, but which also exhibit small data footprints. For example,the sparse data maps described in detail below may require significantlyless storage space and data transfer bandwidth as compared with digitalmaps including detailed map information, such as image data collectedalong a road.

For example, rather than storing detailed representations of a roadsegment, the sparse data map may store three-dimensional polynomialrepresentations of preferred vehicle paths along a road. These paths mayrequire very little data storage space. Further, in the described sparsedata maps, landmarks may be identified and included in the sparse maproad model to aid in navigation. These landmarks may be located at anyspacing suitable for enabling vehicle navigation, but in some cases,such landmarks need not be identified and included in the model at highdensities and short spacings. Rather, in some cases, navigation may bepossible based on landmarks that are spaced apart by at least 50 meters,at least 100 meters, at least 500 meters, at least 1 kilometer, or atleast 2 kilometers. As will be discussed in more detail in othersections, the sparse map may be generated based on data collected ormeasured by vehicles equipped with various sensors and devices, such asimage capture devices, Global Positioning System sensors, motionsensors, etc., as the vehicles travel along roadways. In some cases, thesparse map may be generated based on data collected during multipledrives of one or more vehicles along a particular roadway. Generating asparse map using multiple drives of one or more vehicles may be referredto as “crowdsourcing” a sparse map.

Consistent with disclosed embodiments, an autonomous vehicle system mayuse a sparse map for navigation. For example, the disclosed systems andmethods may distribute a sparse map for generating a road navigationmodel for an autonomous vehicle and may navigate an autonomous vehiclealong a road segment using a sparse map and/or a generated roadnavigation model. Sparse maps consistent with the present disclosure mayinclude one or more three-dimensional contours that may representpredetermined trajectories that autonomous vehicles may traverse as theymove along associated road segments.

Sparse maps consistent with the present disclosure may also include datarepresenting one or more road features. Such road features may includerecognized landmarks, road signature profiles, and any otherroad-related features useful in navigating a vehicle. Sparse mapsconsistent with the present disclosure may enable autonomous navigationof a vehicle based on relatively small amounts of data included in thesparse map. For example, rather than including detailed representationsof a road, such as road edges, road curvature, images associated withroad segments, or data detailing other physical features associated witha road segment, the disclosed embodiments of the sparse map may requirerelatively little storage space (and relatively little bandwidth whenportions of the sparse map are transferred to a vehicle) but may stilladequately provide for autonomous vehicle navigation. The small datafootprint of the disclosed sparse maps, discussed in further detailbelow, may be achieved in some embodiments by storing representations ofroad-related elements that require small amounts of data but stillenable autonomous navigation.

For example, rather than storing detailed representations of variousaspects of a road, the disclosed sparse maps may store polynomialrepresentations of one or more trajectories that a vehicle may followalong the road. Thus, rather than storing (or having to transfer)details regarding the physical nature of the road to enable navigationalong the road, using the disclosed sparse maps, a vehicle may benavigated along a particular road segment without, in some cases, havingto interpret physical aspects of the road, but rather, by aligning itspath of travel with a trajectory (e.g., a polynomial spline) along theparticular road segment. In this way, the vehicle may be navigated basedmainly upon the stored trajectory (e.g., a polynomial spline) that mayrequire much less storage space than an approach involving storage ofroadway images, road parameters, road layout, etc.

In addition to the stored polynomial representations of trajectoriesalong a road segment, the disclosed sparse maps may also include smalldata objects that may represent a road feature. In some embodiments, thesmall data objects may include digital signatures, which are derivedfrom a digital image (or a digital signal) that was obtained by a sensor(e.g., a camera or other sensor, such as a suspension sensor) onboard avehicle traveling along the road segment. The digital signature may havea reduced size relative to the signal that was acquired by the sensor.In some embodiments, the digital signature may be created to becompatible with a classifier function that is configured to detect andto identify the road feature from the signal that is acquired by thesensor, for example, during a subsequent drive. In some embodiments, adigital signature may be created such that the digital signature has afootprint that is as small as possible, while retaining the ability tocorrelate or match the road feature with the stored signature based onan image (or a digital signal generated by a sensor, if the storedsignature is not based on an image and/or includes other data) of theroad feature that is captured by a camera onboard a vehicle travelingalong the same road segment at a subsequent time.

In some embodiments, a size of the data objects may be furtherassociated with a uniqueness of the road feature. For example, for aroad feature that is detectable by a camera onboard a vehicle, and wherethe camera system onboard the vehicle is coupled to a classifier that iscapable of distinguishing the image data corresponding to that roadfeature as being associated with a particular type of road feature, forexample, a road sign, and where such a road sign is locally unique inthat area (e.g., there is no identical road sign or road sign of thesame type nearby), it may be sufficient to store data indicating thetype of the road feature and its location.

As will be discussed in further detail below, road features (e.g.,landmarks along a road segment) may be stored as small data objects thatmay represent a road feature in relatively few bytes, while at the sametime providing sufficient information for recognizing and using such afeature for navigation. In one example, a road sign may be identified asa recognized landmark on which navigation of a vehicle may be based. Arepresentation of the road sign may be stored in the sparse map toinclude, e.g., a few bytes of data indicating a type of landmark (e.g.,a stop sign) and a few bytes of data indicating a location of thelandmark (e.g., coordinates). Navigating based on such data-lightrepresentations of the landmarks (e.g., using representations sufficientfor locating, recognizing, and navigating based upon the landmarks) mayprovide a desired level of navigational functionality associated withsparse maps without significantly increasing the data overheadassociated with the sparse maps. This lean representation of landmarks(and other road features) may take advantage of the sensors andprocessors included onboard such vehicles that are configured to detect,identify, and/or classify certain road features.

When, for example, a sign or even a particular type of a sign is locallyunique (e.g., when there is no other sign or no other sign of the sametype) in a given area, the sparse map may use data indicating a type ofa landmark (a sign or a specific type of sign), and during navigation(e.g., autonomous navigation) when a camera onboard an autonomousvehicle captures an image of the area including a sign (or of a specifictype of sign), the processor may process the image, detect the sign (ifindeed present in the image), classify the image as a sign (or as aspecific type of sign), and correlate the location of the image with thelocation of the sign as stored in the sparse map.

The sparse map may include any suitable representation of objectsidentified along a road segment. In some cases, the objects may bereferred to as semantic objects or non-semantic objects. Semanticobjects may include, for example, objects associated with apredetermined type classification. This type classification may beuseful in reducing the amount of data required to describe the semanticobject recognized in an environment, which can be beneficial both in theharvesting phase (e.g., to reduce costs associated with bandwidth usefor transferring drive information from a plurality of harvestingvehicles to a server) and during the navigation phase (e.g., reductionof map data can speed transfer of map tiles from a server to anavigating vehicle and can also reduce costs associated with bandwidthuse for such transfers). Semantic object classification types may beassigned to any type of objects or features that are expected to beencountered along a roadway.

Semantic objects may further be divided into two or more logical groups.For example, in some cases, one group of semantic object types may beassociated with predetermined dimensions. Such semantic objects mayinclude certain speed limit signs, yield signs, merge signs, stop signs,traffic lights, directional arrows on a roadway, manhole covers, or anyother type of object that may be associated with a standardized size.One benefit offered by such semantic objects is that very little datamay be needed to represent/fully define the objects. For example, if astandardized size of a speed limit size is known, then a harvestingvehicle may need only identify (through analysis of a captured image)the presence of a speed limit sign (a recognized type) along with anindication of a position of the detected speed limit sign (e.g., a 2Dposition in the captured image (or, alternatively, a 3D position in realworld coordinates) of a center of the sign or a certain corner of thesign) to provide sufficient information for map generation on the serverside. Where 2D image positions are transmitted to the server, a positionassociated with the captured image where the sign was detected may alsobe transmitted so the server can determine a real-world position of thesign (e.g., through structure in motion techniques using multiplecaptured images from one or more harvesting vehicles). Even with thislimited information (requiring just a few bytes to define each detectedobject), the server may construct the map including a fully representedspeed limit sign based on the type classification (representative of aspeed limit sign) received from one or more harvesting vehicles alongwith the position information for the detected sign.

Semantic objects may also include other recognized object or featuretypes that are not associated with certain standardized characteristics.Such objects or features may include potholes, tar seams, light poles,non-standardized signs, curbs, trees, tree branches, or any other typeof recognized object type with one or more variable characteristics(e.g., variable dimensions). In such cases, in addition to transmittingto a server an indication of the detected object or feature type (e.g.,pothole, pole, etc.) and position information for the detected object orfeature, a harvesting vehicle may also transmit an indication of a sizeof the object or feature. The size may be expressed in 2D imagedimensions (e.g., with a bounding box or one or more dimension values)or real-world dimensions (determined through structure in motioncalculations, based on LIDAR or RADAR system outputs, based on trainedneural network outputs, etc.).

Non-semantic objects or features may include any detectable objects orfeatures that fall outside of a recognized category or type, but thatstill may provide valuable information in map generation. In some cases,such non-semantic features may include a detected corner of a buildingor a corner of a detected window of a building, a unique stone or objectnear a roadway, a concrete splatter in a roadway shoulder, or any otherdetectable object or feature. Upon detecting such an object or featureone or more harvesting vehicles may transmit to a map generation servera location of one or more points (2D image points or 3D real worldpoints) associated with the detected object/feature. Additionally, acompressed or simplified image segment (e.g., an image hash) may begenerated for a region of the captured image including the detectedobject or feature. This image hash may be calculated based on apredetermined image processing algorithm and may form an effectivesignature for the detected non-semantic object or feature. Such asignature may be useful for navigation relative to a sparse mapincluding the non-semantic feature or object, as a vehicle traversingthe roadway may apply an algorithm similar to the algorithm used togenerate the image hash in order to confirm/verify the presence in acaptured image of the mapped non-semantic feature or object. Using thistechnique, non-semantic features may add to the richness of the sparsemaps (e.g., to enhance their usefulness in navigation) without addingsignificant data overhead.

As noted, target trajectories may be stored in the sparse map. Thesetarget trajectories (e.g., 3D splines) may represent the preferred orrecommended paths for each available lane of a roadway, each validpathway through a junction, for merges and exits, etc. In addition totarget trajectories, other road feature may also be detected, harvested,and incorporated in the sparse maps in the form of representativesplines. Such features may include, for example, road edges, lanemarkings, curbs, guardrails, or any other objects or features thatextend along a roadway or road segment.

Generating a Sparse Map

In some embodiments, a sparse map may include at least one linerepresentation of a road surface feature extending along a road segmentand a plurality of landmarks associated with the road segment. Incertain aspects, the sparse map may be generated via “crowdsourcing,”for example, through image analysis of a plurality of images acquired asone or more vehicles traverse the road segment.

FIG. 8 shows a sparse map 800 that one or more vehicles, e.g., vehicle200 (which may be an autonomous vehicle), may access for providingautonomous vehicle navigation. Sparse map 800 may be stored in a memory,such as memory 140 or 150. Such memory devices may include any types ofnon-transitory storage devices or computer-readable media. For example,in some embodiments, memory 140 or 150 may include hard drives, compactdiscs, flash memory, magnetic based memory devices, optical based memorydevices, etc. In some embodiments, sparse map 800 may be stored in adatabase (e.g., map database 160) that may be stored in memory 140 or150, or other types of storage devices.

In some embodiments, sparse map 800 may be stored on a storage device ora non-transitory computer-readable medium provided onboard vehicle 200(e.g., a storage device included in a navigation system onboard vehicle200). A processor (e.g., processing unit 110) provided on vehicle 200may access sparse map 800 stored in the storage device orcomputer-readable medium provided onboard vehicle 200 in order togenerate navigational instructions for guiding the autonomous vehicle200 as the vehicle traverses a road segment.

Sparse map 800 need not be stored locally with respect to a vehicle,however. In some embodiments, sparse map 800 may be stored on a storagedevice or computer-readable medium provided on a remote server thatcommunicates with vehicle 200 or a device associated with vehicle 200. Aprocessor (e.g., processing unit 110) provided on vehicle 200 mayreceive data included in sparse map 800 from the remote server and mayexecute the data for guiding the autonomous driving of vehicle 200. Insuch embodiments, the remote server may store all of sparse map 800 oronly a portion thereof. Accordingly, the storage device orcomputer-readable medium provided onboard vehicle 200 and/or onboard oneor more additional vehicles may store the remaining portion(s) of sparsemap 800.

Furthermore, in such embodiments, sparse map 800 may be made accessibleto a plurality of vehicles traversing various road segments (e.g., tens,hundreds, thousands, or millions of vehicles, etc.). It should be notedalso that sparse map 800 may include multiple sub-maps. For example, insome embodiments, sparse map 800 may include hundreds, thousands,millions, or more, of sub-maps (e.g., map tiles) that may be used innavigating a vehicle. Such sub-maps may be referred to as local maps ormap tiles, and a vehicle traveling along a roadway may access any numberof local maps relevant to a location in which the vehicle is traveling.The local map sections of sparse map 800 may be stored with a GlobalNavigation Satellite System (GNSS) key as an index to the database ofsparse map 800. Thus, while computation of steering angles fornavigating a host vehicle in the present system may be performed withoutreliance upon a GNSS position of the host vehicle, road features, orlandmarks, such GNSS information may be used for retrieval of relevantlocal maps.

In general, sparse map 800 may be generated based on data (e.g., driveinformation) collected from one or more vehicles as they travel alongroadways. For example, using sensors aboard the one or more vehicles(e.g., cameras, speedometers, GPS, accelerometers, etc.), thetrajectories that the one or more vehicles travel along a roadway may berecorded, and the polynomial representation of a preferred trajectoryfor vehicles making subsequent trips along the roadway may be determinedbased on the collected trajectories travelled by the one or morevehicles. Similarly, data collected by the one or more vehicles may aidin identifying potential landmarks along a particular roadway. Datacollected from traversing vehicles may also be used to identify roadprofile information, such as road width profiles, road roughnessprofiles, traffic line spacing profiles, road conditions, etc. Using thecollected information, sparse map 800 may be generated and distributed(e.g., for local storage or via on-the-fly data transmission) for use innavigating one or more autonomous vehicles. However, in someembodiments, map generation may not end upon initial generation of themap. As will be discussed in greater detail below, sparse map 800 may becontinuously or periodically updated based on data collected fromvehicles as those vehicles continue to traverse roadways included insparse map 800.

Data recorded in sparse map 800 may include position information basedon Global Positioning System (GPS) data. For example, locationinformation may be included in sparse map 800 for various map elements,including, for example, landmark locations, road profile locations, etc.Locations for map elements included in sparse map 800 may be obtainedusing GPS data collected from vehicles traversing a roadway. Forexample, a vehicle passing an identified landmark may determine alocation of the identified landmark using GPS position informationassociated with the vehicle and a determination of a location of theidentified landmark relative to the vehicle (e.g., based on imageanalysis of data collected from one or more cameras on board thevehicle). Such location determinations of an identified landmark (or anyother feature included in sparse map 800) may be repeated as additionalvehicles pass the location of the identified landmark. Some or all ofthe additional location determinations may be used to refine thelocation information stored in sparse map 800 relative to the identifiedlandmark. For example, in some embodiments, multiple positionmeasurements relative to a particular feature stored in sparse map 800may be averaged together. Any other mathematical operations, however,may also be used to refine a stored location of a map element based on aplurality of determined locations for the map element.

In a particular example, harvesting vehicles may traverse a particularroad segment. Each harvesting vehicle captures images of theirrespective environments. The images may be collected at any suitableframe capture rate (e.g., 9 Hz, etc.). Image analysis processor(s)aboard each harvesting vehicle analyze the captured images to detect thepresence of semantic and/or non-semantic features/objects. At a highlevel, the harvesting vehicles transmit to a mapping-server indicationsof detections of the semantic and/or non-semantic objects/features alongwith positions associated with those objects/features. In more detail,type indicators, dimension indicators, etc. may be transmitted togetherwith the position information. The position information may include anysuitable information for enabling the mapping server to aggregate thedetected objects/features into a sparse map useful in navigation. Insome cases, the position information may include one or more 2D imagepositions (e.g., X-Y pixel locations) in a captured image where thesemantic or non-semantic features/objects were detected. Such imagepositions may correspond to a center of the feature/object, a corner,etc. In this scenario, to aid the mapping server in reconstructing thedrive information and aligning the drive information from multipleharvesting vehicles, each harvesting vehicle may also provide the serverwith a location (e.g., a GPS location) where each image was captured.

In other cases, the harvesting vehicle may provide to the server one ormore 3D real world points associated with the detected objects/features.Such 3D points may be relative to a predetermined origin (such as anorigin of a drive segment) and may be determined through any suitabletechnique. In some cases, a structure in motion technique may be used todetermine the 3D real world position of a detected object/feature. Forexample, a certain object such as a particular speed limit sign may bedetected in two or more captured images. Using information such as theknown ego motion (speed, trajectory, GPS position, etc.) of theharvesting vehicle between the captured images, along with observedchanges of the speed limit sign in the captured images (change in X-Ypixel location, change in size, etc.), the real-world position of one ormore points associated with the speed limit sign may be determined andpassed along to the mapping server. Such an approach is optional, as itrequires more computation on the part of the harvesting vehicle systems.The sparse map of the disclosed embodiments may enable autonomousnavigation of a vehicle using relatively small amounts of stored data.In some embodiments, sparse map 800 may have a data density (e.g.,including data representing the target trajectories, landmarks, and anyother stored road features) of less than 2 MB per kilometer of roads,less than 1 MB per kilometer of roads, less than 500 kB per kilometer ofroads, or less than 100 kB per kilometer of roads. In some embodiments,the data density of sparse map 800 may be less than 10 kB per kilometerof roads or even less than 2 kB per kilometer of roads (e.g., 1.6 kB perkilometer), or no more than 10 kB per kilometer of roads, or no morethan 20 kB per kilometer of roads. In some embodiments, most, if notall, of the roadways of the United States may be navigated autonomouslyusing a sparse map having a total of 4 GB or less of data. These datadensity values may represent an average over an entire sparse map 800,over a local map within sparse map 800, and/or over a particular roadsegment within sparse map 800.

As noted, sparse map 800 may include representations of a plurality oftarget trajectories 810 for guiding autonomous driving or navigationalong a road segment. Such target trajectories may be stored asthree-dimensional splines. The target trajectories stored in sparse map800 may be determined based on two or more reconstructed trajectories ofprior traversals of vehicles along a particular road segment, forexample. A road segment may be associated with a single targettrajectory or multiple target trajectories. For example, on a two laneroad, a first target trajectory may be stored to represent an intendedpath of travel along the road in a first direction, and a second targettrajectory may be stored to represent an intended path of travel alongthe road in another direction (e.g., opposite to the first direction).Additional target trajectories may be stored with respect to aparticular road segment. For example, on a multi-lane road one or moretarget trajectories may be stored representing intended paths of travelfor vehicles in one or more lanes associated with the multi-lane road.In some embodiments, each lane of a multi-lane road may be associatedwith its own target trajectory. In other embodiments, there may be fewertarget trajectories stored than lanes present on a multi-lane road. Insuch cases, a vehicle navigating the multi-lane road may use any of thestored target trajectories to guides its navigation by taking intoaccount an amount of lane offset from a lane for which a targettrajectory is stored (e.g., if a vehicle is traveling in the left mostlane of a three lane highway, and a target trajectory is stored only forthe middle lane of the highway, the vehicle may navigate using thetarget trajectory of the middle lane by accounting for the amount oflane offset between the middle lane and the left-most lane whengenerating navigational instructions).

In some embodiments, the target trajectory may represent an ideal paththat a vehicle should take as the vehicle travels. The target trajectorymay be located, for example, at an approximate center of a lane oftravel. In other cases, the target trajectory may be located elsewhererelative to a road segment. For example, a target trajectory mayapproximately coincide with a center of a road, an edge of a road, or anedge of a lane, etc. In such cases, navigation based on the targettrajectory may include a determined amount of offset to be maintainedrelative to the location of the target trajectory. Moreover, in someembodiments, the determined amount of offset to be maintained relativeto the location of the target trajectory may differ based on a type ofvehicle (e.g., a passenger vehicle including two axles may have adifferent offset from a truck including more than two axles along atleast a portion of the target trajectory).

Sparse map 800 may also include data relating to a plurality ofpredetermined landmarks 820 associated with particular road segments,local maps, etc. As discussed in greater detail below, these landmarksmay be used in navigation of the autonomous vehicle. For example, insome embodiments, the landmarks may be used to determine a currentposition of the vehicle relative to a stored target trajectory. Withthis position information, the autonomous vehicle may be able to adjusta heading direction to match a direction of the target trajectory at thedetermined location.

The plurality of landmarks 820 may be identified and stored in sparsemap 800 at any suitable spacing. In some embodiments, landmarks may bestored at relatively high densities (e.g., every few meters or more). Insome embodiments, however, significantly larger landmark spacing valuesmay be employed. For example, in sparse map 800, identified (orrecognized) landmarks may be spaced apart by 10 meters, 20 meters, 50meters, 100 meters, 1 kilometer, or 2 kilometers. In some cases, theidentified landmarks may be located at distances of even more than 2kilometers apart.

Between landmarks, and therefore between determinations of vehicleposition relative to a target trajectory, the vehicle may navigate basedon dead reckoning in which the vehicle uses sensors to determine its egomotion and estimate its position relative to the target trajectory.Because errors may accumulate during navigation by dead reckoning, overtime the position determinations relative to the target trajectory maybecome increasingly less accurate. The vehicle may use landmarksoccurring in sparse map 800 (and their known locations) to remove thedead reckoning-induced errors in position determination. In this way,the identified landmarks included in sparse map 800 may serve asnavigational anchors from which an accurate position of the vehiclerelative to a target trajectory may be determined. Because a certainamount of error may be acceptable in position location, an identifiedlandmark need not always be available to an autonomous vehicle. Rather,suitable navigation may be possible even based on landmark spacings, asnoted above, of 10 meters, 20 meters, 50 meters, 100 meters, 500 meters,1 kilometer, 2 kilometers, or more. In some embodiments, a density of 1identified landmark every 1 km of road may be sufficient to maintain alongitudinal position determination accuracy within 1 m. Thus, not everypotential landmark appearing along a road segment need be stored insparse map 800.

Moreover, in some embodiments, lane markings may be used forlocalization of the vehicle during landmark spacings. By using lanemarkings during landmark spacings, the accumulation of errors duringnavigation by dead reckoning may be minimized.

In addition to target trajectories and identified landmarks, sparse map800 may include information relating to various other road features. Forexample, FIG. 9A illustrates a representation of curves along aparticular road segment that may be stored in sparse map 800. In someembodiments, a single lane of a road may be modeled by athree-dimensional polynomial description of left and right sides of theroad. Such polynomials representing left and right sides of a singlelane are shown in FIG. 9A. Regardless of how many lanes a road may have,the road may be represented using polynomials in a way similar to thatillustrated in FIG. 9A. For example, left and right sides of amulti-lane road may be represented by polynomials similar to those shownin FIG. 9A, and intermediate lane markings included on a multi-lane road(e.g., dashed markings representing lane boundaries, solid yellow linesrepresenting boundaries between lanes traveling in different directions,etc.) may also be represented using polynomials such as those shown inFIG. 9A.

As shown in FIG. 9A, a lane 900 may be represented using polynomials(e.g., a first order, second order, third order, or any suitable orderpolynomials). For illustration, lane 900 is shown as a two-dimensionallane and the polynomials are shown as two-dimensional polynomials. Asdepicted in FIG. 9A, lane 900 includes a left side 910 and a right side920. In some embodiments, more than one polynomial may be used torepresent a location of each side of the road or lane boundary. Forexample, each of left side 910 and right side 920 may be represented bya plurality of polynomials of any suitable length. In some cases, thepolynomials may have a length of about 100 m, although other lengthsgreater than or less than 100 m may also be used. Additionally, thepolynomials can overlap with one another in order to facilitate seamlesstransitions in navigating based on subsequently encountered polynomialsas a host vehicle travels along a roadway. For example, each of leftside 910 and right side 920 may be represented by a plurality of thirdorder polynomials separated into segments of about 100 meters in length(an example of the first predetermined range), and overlapping eachother by about 50 meters. The polynomials representing the left side 910and the right side 920 may or may not have the same order. For example,in some embodiments, some polynomials may be second order polynomials,some may be third order polynomials, and some may be fourth orderpolynomials.

In the example shown in FIG. 9A, left side 910 of lane 900 isrepresented by two groups of third order polynomials. The first groupincludes polynomial segments 911, 912, and 913. The second groupincludes polynomial segments 914, 915, and 916. The two groups, whilesubstantially parallel to each other, follow the locations of theirrespective sides of the road. Polynomial segments 911, 912, 913, 914,915, and 916 have a length of about 100 meters and overlap adjacentsegments in the series by about 50 meters. As noted previously, however,polynomials of different lengths and different overlap amounts may alsobe used. For example, the polynomials may have lengths of 500 m, 1 km,or more, and the overlap amount may vary from 0 to 50 m, 50 m to 100 m,or greater than 100 m. Additionally, while FIG. 9A is shown asrepresenting polynomials extending in 2D space (e.g., on the surface ofthe paper), it is to be understood that these polynomials may representcurves extending in three dimensions (e.g., including a heightcomponent) to represent elevation changes in a road segment in additionto X-Y curvature. In the example shown in FIG. 9A, right side 920 oflane 900 is further represented by a first group having polynomialsegments 921, 922, and 923 and a second group having polynomial segments924, 925, and 926.

Returning to the target trajectories of sparse map 800, FIG. 9B shows athree-dimensional polynomial representing a target trajectory for avehicle traveling along a particular road segment. The target trajectoryrepresents not only the X-Y path that a host vehicle should travel alonga particular road segment, but also the elevation change that the hostvehicle will experience when traveling along the road segment. Thus,each target trajectory in sparse map 800 may be represented by one ormore three-dimensional polynomials, like the three-dimensionalpolynomial 950 shown in FIG. 9B. Sparse map 800 may include a pluralityof trajectories (e.g., millions or billions or more to representtrajectories of vehicles along various road segments along roadwaysthroughout the world). In some embodiments, each target trajectory maycorrespond to a spline connecting three-dimensional polynomial segments.

Regarding the data footprint of polynomial curves stored in sparse map800, in some embodiments, each third degree polynomial may berepresented by four parameters, each requiring four bytes of data.Suitable representations may be obtained with third degree polynomialsrequiring about 192 bytes of data for every 100 m. This may translate toapproximately 200 kB per hour in data usage/transfer requirements for ahost vehicle traveling approximately 100 km/hr.

Sparse map 800 may describe the lanes network using a combination ofgeometry descriptors and meta-data. The geometry may be described bypolynomials or splines as described above. The meta-data may describethe number of lanes, special characteristics (such as a car pool lane),and possibly other sparse labels. The total footprint of such indicatorsmay be negligible.

Accordingly, a sparse map according to embodiments of the presentdisclosure may include at least one line representation of a roadsurface feature extending along the road segment, each linerepresentation representing a path along the road segment substantiallycorresponding with the road surface feature. In some embodiments, asdiscussed above, the at least one line representation of the roadsurface feature may include a spline, a polynomial representation, or acurve. Furthermore, in some embodiments, the road surface feature mayinclude at least one of a road edge or a lane marking. Moreover, asdiscussed below with respect to “crowdsourcing,” the road surfacefeature may be identified through image analysis of a plurality ofimages acquired as one or more vehicles traverse the road segment.

As previously noted, sparse map 800 may include a plurality ofpredetermined landmarks associated with a road segment. Rather thanstoring actual images of the landmarks and relying, for example, onimage recognition analysis based on captured images and stored images,each landmark in sparse map 800 may be represented and recognized usingless data than a stored, actual image would require. Data representinglandmarks may still include sufficient information for describing oridentifying the landmarks along a road. Storing data describingcharacteristics of landmarks, rather than the actual images oflandmarks, may reduce the size of sparse map 800.

FIG. 10 illustrates examples of types of landmarks that may berepresented in sparse map 800. The landmarks may include any visible andidentifiable objects along a road segment. The landmarks may be selectedsuch that they are fixed and do not change often with respect to theirlocations and/or content. The landmarks included in sparse map 800 maybe useful in determining a location of vehicle 200 with respect to atarget trajectory as the vehicle traverses a particular road segment.Examples of landmarks may include traffic signs, directional signs,general signs (e.g., rectangular signs), roadside fixtures (e.g.,lampposts, reflectors, etc.), and any other suitable category. In someembodiments, lane marks on the road, may also be included as landmarksin sparse map 800.

Examples of landmarks shown in FIG. 10 include traffic signs,directional signs, roadside fixtures, and general signs. Traffic signsmay include, for example, speed limit signs (e.g., speed limit sign1000), yield signs (e.g., yield sign 1005), route number signs (e.g.,route number sign 1010), traffic light signs (e.g., traffic light sign1015), stop signs (e.g., stop sign 1020). Directional signs may includea sign that includes one or more arrows indicating one or moredirections to different places. For example, directional signs mayinclude a highway sign 1025 having arrows for directing vehicles todifferent roads or places, an exit sign 1030 having an arrow directingvehicles off a road, etc. Accordingly, at least one of the plurality oflandmarks may include a road sign.

General signs may be unrelated to traffic. For example, general signsmay include billboards used for advertisement, or a welcome boardadjacent a border between two countries, states, counties, cities, ortowns. FIG. 10 shows a general sign 1040 (“Joe's Restaurant”). Althoughgeneral sign 1040 may have a rectangular shape, as shown in FIG. 10,general sign 1040 may have other shapes, such as square, circle,triangle, etc.

Landmarks may also include roadside fixtures. Roadside fixtures may beobjects that are not signs, and may not be related to traffic ordirections. For example, roadside fixtures may include lampposts (e.g.,lamppost 1035), power line posts, traffic light posts, etc.

Landmarks may also include beacons that may be specifically designed forusage in an autonomous vehicle navigation system. For example, suchbeacons may include stand-alone structures placed at predeterminedintervals to aid in navigating a host vehicle. Such beacons may alsoinclude visual/graphical information added to existing road signs (e.g.,icons, emblems, bar codes, etc.) that may be identified or recognized bya vehicle traveling along a road segment. Such beacons may also includeelectronic components. In such embodiments, electronic beacons (e.g.,RFID tags, etc.) may be used to transmit non-visual information to ahost vehicle. Such information may include, for example, landmarkidentification and/or landmark location information that a host vehiclemay use in determining its position along a target trajectory.

In some embodiments, the landmarks included in sparse map 800 may berepresented by a data object of a predetermined size. The datarepresenting a landmark may include any suitable parameters foridentifying a particular landmark. For example, in some embodiments,landmarks stored in sparse map 800 may include parameters such as aphysical size of the landmark (e.g., to support estimation of distanceto the landmark based on a known size/scale), a distance to a previouslandmark, lateral offset, height, a type code (e.g., a landmarktype—what type of directional sign, traffic sign, etc.), a GPScoordinate (e.g., to support global localization), and any othersuitable parameters. Each parameter may be associated with a data size.For example, a landmark size may be stored using 8 bytes of data. Adistance to a previous landmark, a lateral offset, and height may bespecified using 12 bytes of data. A type code associated with a landmarksuch as a directional sign or a traffic sign may require about 2 bytesof data. For general signs, an image signature enabling identificationof the general sign may be stored using 50 bytes of data storage. Thelandmark GPS position may be associated with 16 bytes of data storage.These data sizes for each parameter are examples only, and other datasizes may also be used. Representing landmarks in sparse map 800 in thismanner may offer a lean solution for efficiently representing landmarksin the database. In some embodiments, objects may be referred to asstandard semantic objects or non-standard semantic objects. A standardsemantic object may include any class of object for which there's astandardized set of characteristics (e.g., speed limit signs, warningsigns, directional signs, traffic lights, etc. having known dimensionsor other characteristics). A non-standard semantic object may includeany object that is not associated with a standardized set ofcharacteristics (e.g., general advertising signs, signs identifyingbusiness establishments, potholes, trees, etc. that may have variabledimensions). Each non-standard semantic object may be represented with38 bytes of data (e.g., 8 bytes for size; 12 bytes for distance toprevious landmark, lateral offset, and height; 2 bytes for a type code;and 16 bytes for position coordinates). Standard semantic objects may berepresented using even less data, as size information may not be neededby the mapping server to fully represent the object in the sparse map.

Sparse map 800 may use a tag system to represent landmark types. In somecases, each traffic sign or directional sign may be associated with itsown tag, which may be stored in the database as part of the landmarkidentification. For example, the database may include on the order of1000 different tags to represent various traffic signs and on the orderof about 10000 different tags to represent directional signs. Of course,any suitable number of tags may be used, and additional tags may becreated as needed. General purpose signs may be represented in someembodiments using less than about 100 bytes (e.g., about 86 bytesincluding 8 bytes for size; 12 bytes for distance to previous landmark,lateral offset, and height; 50 bytes for an image signature; and 16bytes for GPS coordinates).

Thus, for semantic road signs not requiring an image signature, the datadensity impact to sparse map 800, even at relatively high landmarkdensities of about 1 per 50 m, may be on the order of about 760 bytesper kilometer (e.g., 20 landmarks per km×38 bytes per landmark=760bytes). Even for general purpose signs including an image signaturecomponent, the data density impact is about 1.72 kB per km (e.g., 20landmarks per km×86 bytes per landmark=1,720 bytes). For semantic roadsigns, this equates to about 76 kB per hour of data usage for a vehicletraveling 100 km/hr. For general purpose signs, this equates to about170 kB per hour for a vehicle traveling 100 km/hr. It should be notedthat in some environments (e.g., urban environments) there may be a muchhigher density of detected objects available for inclusion in the sparsemap (perhaps more than one per meter). In some embodiments, a generallyrectangular object, such as a rectangular sign, may be represented insparse map 800 by no more than 100 bytes of data. The representation ofthe generally rectangular object (e.g., general sign 1040) in sparse map800 may include a condensed image signature or image hash (e.g.,condensed image signature 1045) associated with the generallyrectangular object. This condensed image signature/image hash may bedetermined using any suitable image hashing algorithm and may be used,for example, to aid in identification of a general purpose sign, forexample, as a recognized landmark. Such a condensed image signature(e.g., image information derived from actual image data representing anobject) may avoid a need for storage of an actual image of an object ora need for comparative image analysis performed on actual images inorder to recognize landmarks.

Referring to FIG. 10, sparse map 800 may include or store a condensedimage signature 1045 associated with a general sign 1040, rather than anactual image of general sign 1040. For example, after an image capturedevice (e.g., image capture device 122, 124, or 126) captures an imageof general sign 1040, a processor (e.g., image processor 190 or anyother processor that can process images either aboard or remotelylocated relative to a host vehicle) may perform an image analysis toextract/create condensed image signature 1045 that includes a uniquesignature or pattern associated with general sign 1040. In oneembodiment, condensed image signature 1045 may include a shape, colorpattern, a brightness pattern, or any other feature that may beextracted from the image of general sign 1040 for describing generalsign 1040.

For example, in FIG. 10, the circles, triangles, and stars shown incondensed image signature 1045 may represent areas of different colors.The pattern represented by the circles, triangles, and stars may bestored in sparse map 800, e.g., within the 50 bytes designated toinclude an image signature. Notably, the circles, triangles, and starsare not necessarily meant to indicate that such shapes are stored aspart of the image signature. Rather, these shapes are meant toconceptually represent recognizable areas having discernible colordifferences, textual areas, graphical shapes, or other variations incharacteristics that may be associated with a general purpose sign. Suchcondensed image signatures can be used to identify a landmark in theform of a general sign. For example, the condensed image signature canbe used to perform a same-not-same analysis based on a comparison of astored condensed image signature with image data captured, for example,using a camera onboard an autonomous vehicle.

Accordingly, the plurality of landmarks may be identified through imageanalysis of the plurality of images acquired as one or more vehiclestraverse the road segment. As explained below with respect to“crowdsourcing,” in some embodiments, the image analysis to identify theplurality of landmarks may include accepting potential landmarks when aratio of images in which the landmark does appear to images in which thelandmark does not appear exceeds a threshold. Furthermore, in someembodiments, the image analysis to identify the plurality of landmarksmay include rejecting potential landmarks when a ratio of images inwhich the landmark does not appear to images in which the landmark doesappear exceeds a threshold.

Returning to the target trajectories a host vehicle may use to navigatea particular road segment, FIG. 11A shows polynomial representationstrajectories capturing during a process of building or maintainingsparse map 800. A polynomial representation of a target trajectoryincluded in sparse map 800 may be determined based on two or morereconstructed trajectories of prior traversals of vehicles along thesame road segment. In some embodiments, the polynomial representation ofthe target trajectory included in sparse map 800 may be an aggregationof two or more reconstructed trajectories of prior traversals ofvehicles along the same road segment. In some embodiments, thepolynomial representation of the target trajectory included in sparsemap 800 may be an average of the two or more reconstructed trajectoriesof prior traversals of vehicles along the same road segment. Othermathematical operations may also be used to construct a targettrajectory along a road path based on reconstructed trajectoriescollected from vehicles traversing along a road segment.

As shown in FIG. 11A, a road segment 1100 may be travelled by a numberof vehicles 200 at different times. Each vehicle 200 may collect datarelating to a path that the vehicle took along the road segment. Thepath traveled by a particular vehicle may be determined based on cameradata, accelerometer information, speed sensor information, and/or GPSinformation, among other potential sources. Such data may be used toreconstruct trajectories of vehicles traveling along the road segment,and based on these reconstructed trajectories, a target trajectory (ormultiple target trajectories) may be determined for the particular roadsegment. Such target trajectories may represent a preferred path of ahost vehicle (e.g., guided by an autonomous navigation system) as thevehicle travels along the road segment.

In the example shown in FIG. 11A, a first reconstructed trajectory 1101may be determined based on data received from a first vehicle traversingroad segment 1100 at a first time period (e.g., day 1), a secondreconstructed trajectory 1102 may be obtained from a second vehicletraversing road segment 1100 at a second time period (e.g., day 2), anda third reconstructed trajectory 1103 may be obtained from a thirdvehicle traversing road segment 1100 at a third time period (e.g., day3). Each trajectory 1101, 1102, and 1103 may be represented by apolynomial, such as a three-dimensional polynomial. It should be notedthat in some embodiments, any of the reconstructed trajectories may beassembled onboard the vehicles traversing road segment 1100.

Additionally, or alternatively, such reconstructed trajectories may bedetermined on a server side based on information received from vehiclestraversing road segment 1100. For example, in some embodiments, vehicles200 may transmit data to one or more servers relating to their motionalong road segment 1100 (e.g., steering angle, heading, time, position,speed, sensed road geometry, and/or sensed landmarks, among things). Theserver may reconstruct trajectories for vehicles 200 based on thereceived data. The server may also generate a target trajectory forguiding navigation of autonomous vehicle that will travel along the sameroad segment 1100 at a later time based on the first, second, and thirdtrajectories 1101, 1102, and 1103. While a target trajectory may beassociated with a single prior traversal of a road segment, in someembodiments, each target trajectory included in sparse map 800 may bedetermined based on two or more reconstructed trajectories of vehiclestraversing the same road segment. In FIG. 11A, the target trajectory isrepresented by 1110. In some embodiments, the target trajectory 1110 maybe generated based on an average of the first, second, and thirdtrajectories 1101, 1102, and 1103. In some embodiments, the targettrajectory 1110 included in sparse map 800 may be an aggregation (e.g.,a weighted combination) of two or more reconstructed trajectories.

At the mapping server, the server may receive actual trajectories for aparticular road segment from multiple harvesting vehicles traversing theroad segment. To generate a target trajectory for each valid path alongthe road segment (e.g., each lane, each drive direction, each paththrough a junction, etc.), the received actual trajectories may bealigned. The alignment process may include using detectedobjects/features identified along the road segment along with harvestedpositions of those detected objects/features to correlate the actual,harvested trajectories with one another. Once aligned, an average or“best fit” target trajectory for each available lane, etc. may bedetermined based on the aggregated, correlated/aligned actualtrajectories.

FIGS. 11B and 11C further illustrate the concept of target trajectoriesassociated with road segments present within a geographic region 1111.As shown in FIG. 11B, a first road segment 1120 within geographic region1111 may include a multilane road, which includes two lanes 1122designated for vehicle travel in a first direction and two additionallanes 1124 designated for vehicle travel in a second direction oppositeto the first direction. Lanes 1122 and lanes 1124 may be separated by adouble yellow line 1123. Geographic region 1111 may also include abranching road segment 1130 that intersects with road segment 1120. Roadsegment 1130 may include a two-lane road, each lane being designated fora different direction of travel. Geographic region 1111 may also includeother road features, such as a stop line 1132, a stop sign 1134, a speedlimit sign 1136, and a hazard sign 1138.

As shown in FIG. 11C, sparse map 800 may include a local map 1140including a road model for assisting with autonomous navigation ofvehicles within geographic region 1111. For example, local map 1140 mayinclude target trajectories for one or more lanes associated with roadsegments 1120 and/or 1130 within geographic region 1111. For example,local map 1140 may include target trajectories 1141 and/or 1142 that anautonomous vehicle may access or rely upon when traversing lanes 1122.Similarly, local map 1140 may include target trajectories 1143 and/or1144 that an autonomous vehicle may access or rely upon when traversinglanes 1124. Further, local map 1140 may include target trajectories 1145and/or 1146 that an autonomous vehicle may access or rely upon whentraversing road segment 1130. Target trajectory 1147 represents apreferred path an autonomous vehicle should follow when transitioningfrom lanes 1120 (and specifically, relative to target trajectory 1141associated with a right-most lane of lanes 1120) to road segment 1130(and specifically, relative to a target trajectory 1145 associated witha first side of road segment 1130. Similarly, target trajectory 1148represents a preferred path an autonomous vehicle should follow whentransitioning from road segment 1130 (and specifically, relative totarget trajectory 1146) to a portion of road segment 1124 (andspecifically, as shown, relative to a target trajectory 1143 associatedwith a left lane of lanes 1124.

Sparse map 800 may also include representations of other road-relatedfeatures associated with geographic region 1111. For example, sparse map800 may also include representations of one or more landmarks identifiedin geographic region 1111. Such landmarks may include a first landmark1150 associated with stop line 1132, a second landmark 1152 associatedwith stop sign 1134, a third landmark associated with speed limit sign1154, and a fourth landmark 1156 associated with hazard sign 1138. Suchlandmarks may be used, for example, to assist an autonomous vehicle indetermining its current location relative to any of the shown targettrajectories, such that the vehicle may adjust its heading to match adirection of the target trajectory at the determined location.

In some embodiments, sparse map 800 may also include road signatureprofiles. Such road signature profiles may be associated with anydiscernible/measurable variation in at least one parameter associatedwith a road. For example, in some cases, such profiles may be associatedwith variations in road surface information such as variations insurface roughness of a particular road segment, variations in road widthover a particular road segment, variations in distances between dashedlines painted along a particular road segment, variations in roadcurvature along a particular road segment, etc. FIG. 11D shows anexample of a road signature profile 1160. While profile 1160 mayrepresent any of the parameters mentioned above, or others, in oneexample, profile 1160 may represent a measure of road surface roughness,as obtained, for example, by monitoring one or more sensors providingoutputs indicative of an amount of suspension displacement as a vehicletravels a particular road segment.

Alternatively or concurrently, profile 1160 may represent variation inroad width, as determined based on image data obtained via a cameraonboard a vehicle traveling a particular road segment. Such profiles maybe useful, for example, in determining a particular location of anautonomous vehicle relative to a particular target trajectory. That is,as it traverses a road segment, an autonomous vehicle may measure aprofile associated with one or more parameters associated with the roadsegment. If the measured profile can be correlated/matched with apredetermined profile that plots the parameter variation with respect toposition along the road segment, then the measured and predeterminedprofiles may be used (e.g., by overlaying corresponding sections of themeasured and predetermined profiles) in order to determine a currentposition along the road segment and, therefore, a current positionrelative to a target trajectory for the road segment.

In some embodiments, sparse map 800 may include different trajectoriesbased on different characteristics associated with a user of autonomousvehicles, environmental conditions, and/or other parameters relating todriving. For example, in some embodiments, different trajectories may begenerated based on different user preferences and/or profiles. Sparsemap 800 including such different trajectories may be provided todifferent autonomous vehicles of different users. For example, someusers may prefer to avoid toll roads, while others may prefer to takethe shortest or fastest routes, regardless of whether there is a tollroad on the route. The disclosed systems may generate different sparsemaps with different trajectories based on such different userpreferences or profiles. As another example, some users may prefer totravel in a fast moving lane, while others may prefer to maintain aposition in the central lane at all times.

Different trajectories may be generated and included in sparse map 800based on different environmental conditions, such as day and night,snow, rain, fog, etc. Autonomous vehicles driving under differentenvironmental conditions may be provided with sparse map 800 generatedbased on such different environmental conditions. In some embodiments,cameras provided on autonomous vehicles may detect the environmentalconditions, and may provide such information back to a server thatgenerates and provides sparse maps. For example, the server may generateor update an already generated sparse map 800 to include trajectoriesthat may be more suitable or safer for autonomous driving under thedetected environmental conditions. The update of sparse map 800 based onenvironmental conditions may be performed dynamically as the autonomousvehicles are traveling along roads.

Other different parameters relating to driving may also be used as abasis for generating and providing different sparse maps to differentautonomous vehicles. For example, when an autonomous vehicle istraveling at a high speed, turns may be tighter. Trajectories associatedwith specific lanes, rather than roads, may be included in sparse map800 such that the autonomous vehicle may maintain within a specific laneas the vehicle follows a specific trajectory. When an image captured bya camera onboard the autonomous vehicle indicates that the vehicle hasdrifted outside of the lane (e.g., crossed the lane mark), an action maybe triggered within the vehicle to bring the vehicle back to thedesignated lane according to the specific trajectory.

Crowdsourcing a Sparse Map

The disclosed sparse maps may be efficiently (and passively) generatedthrough the power of crowdsourcing. For example, any private orcommercial vehicle equipped with a camera (e.g., a simple, lowresolution camera regularly included as OEM equipment on today'svehicles) and an appropriate image analysis processor can serve as aharvesting vehicle. No special equipment (e.g., high definition imagingand/or positioning systems) are required. As a result of the disclosedcrowdsourcing technique, the generated sparse maps may be extremelyaccurate and may include extremely refined position information(enabling navigation error limits of 10 cm or less) without requiringany specialized imaging or sensing equipment as input to the mapgeneration process. Crowdsourcing also enables much more rapid (andinexpensive) updates to the generated maps, as new drive information iscontinuously available to the mapping server system from any roadstraversed by private or commercial vehicles minimally equipped to alsoserve as harvesting vehicles. There is no need for designated vehiclesequipped with high-definition imaging and mapping sensors. Therefore,the expense associated with building such specialized vehicles can beavoided. Further, updates to the presently disclosed sparse maps may bemade much more rapidly than systems that rely upon dedicated,specialized mapping vehicles (which by virtue of their expense andspecial equipment are typically limited to a fleet of specializedvehicles of far lower numbers than the number of private or commercialvehicles already available for performing the disclosed harvestingtechniques).

The disclosed sparse maps generated through crowdsourcing may beextremely accurate because they may be generated based on many inputsfrom multiple (10 s, hundreds, millions, etc.) of harvesting vehiclesthat have collected drive information along a particular road segment.For example, every harvesting vehicle that drives along a particularroad segment may record its actual trajectory and may determine positioninformation relative to detected objects/features along the roadsegment. This information is passed along from multiple harvestingvehicles to a server. The actual trajectories are aggregated to generatea refined, target trajectory for each valid drive path along the roadsegment. Additionally, the position information collected from themultiple harvesting vehicles for each of the detected objects/featuresalong the road segment (semantic or non-semantic) can also beaggregated. As a result, the mapped position of each detectedobject/feature may constitute an average of hundreds, thousands, ormillions of individually determined positions for each detectedobject/feature. Such a technique may yield extremely accurate mappedpositions for the detected objects/features.

In some embodiments, the disclosed systems and methods may generate asparse map for autonomous vehicle navigation. For example, disclosedsystems and methods may use crowdsourced data for generation of a sparsemap that one or more autonomous vehicles may use to navigate along asystem of roads. As used herein, “crowdsourcing” means that data arereceived from various vehicles (e.g., autonomous vehicles) travelling ona road segment at different times, and such data are used to generateand/or update the road model, including sparse map tiles. The model orany of its sparse map tiles may, in turn, be transmitted to the vehiclesor other vehicles later travelling along the road segment for assistingautonomous vehicle navigation. The road model may include a plurality oftarget trajectories representing preferred trajectories that autonomousvehicles should follow as they traverse a road segment. The targettrajectories may be the same as a reconstructed actual trajectorycollected from a vehicle traversing a road segment, which may betransmitted from the vehicle to a server. In some embodiments, thetarget trajectories may be different from actual trajectories that oneor more vehicles previously took when traversing a road segment. Thetarget trajectories may be generated based on actual trajectories (e.g.,through averaging or any other suitable operation).

The vehicle trajectory data that a vehicle may upload to a server maycorrespond with the actual reconstructed trajectory for the vehicle ormay correspond to a recommended trajectory, which may be based on orrelated to the actual reconstructed trajectory of the vehicle, but maydiffer from the actual reconstructed trajectory. For example, vehiclesmay modify their actual, reconstructed trajectories and submit (e.g.,recommend) to the server the modified actual trajectories. The roadmodel may use the recommended, modified trajectories as targettrajectories for autonomous navigation of other vehicles.

In addition to trajectory information, other information for potentialuse in building a sparse data map 800 may include information relatingto potential landmark candidates. For example, through crowd sourcing ofinformation, the disclosed systems and methods may identify potentiallandmarks in an environment and refine landmark positions. The landmarksmay be used by a navigation system of autonomous vehicles to determineand/or adjust the position of the vehicle along the target trajectories.

The reconstructed trajectories that a vehicle may generate as thevehicle travels along a road may be obtained by any suitable method. Insome embodiments, the reconstructed trajectories may be developed bystitching together segments of motion for the vehicle, using, e.g., egomotion estimation (e.g., three dimensional translation and threedimensional rotation of the camera, and hence the body of the vehicle).The rotation and translation estimation may be determined based onanalysis of images captured by one or more image capture devices alongwith information from other sensors or devices, such as inertial sensorsand speed sensors. For example, the inertial sensors may include anaccelerometer or other suitable sensors configured to measure changes intranslation and/or rotation of the vehicle body. The vehicle may includea speed sensor that measures a speed of the vehicle.

In some embodiments, the ego motion of the camera (and hence the vehiclebody) may be estimated based on an optical flow analysis of the capturedimages. An optical flow analysis of a sequence of images identifiesmovement of pixels from the sequence of images, and based on theidentified movement, determines motions of the vehicle. The ego motionmay be integrated over time and along the road segment to reconstruct atrajectory associated with the road segment that the vehicle hasfollowed.

Data (e.g., reconstructed trajectories) collected by multiple vehiclesin multiple drives along a road segment at different times may be usedto construct the road model (e.g., including the target trajectories,etc.) included in sparse data map 800. Data collected by multiplevehicles in multiple drives along a road segment at different times mayalso be averaged to increase an accuracy of the model. In someembodiments, data regarding the road geometry and/or landmarks may bereceived from multiple vehicles that travel through the common roadsegment at different times. Such data received from different vehiclesmay be combined to generate the road model and/or to update the roadmodel.

The geometry of a reconstructed trajectory (and also a targettrajectory) along a road segment may be represented by a curve in threedimensional space, which may be a spline connecting three dimensionalpolynomials. The reconstructed trajectory curve may be determined fromanalysis of a video stream or a plurality of images captured by a camerainstalled on the vehicle. In some embodiments, a location is identifiedin each frame or image that is a few meters ahead of the currentposition of the vehicle. This location is where the vehicle is expectedto travel to in a predetermined time period. This operation may berepeated frame by frame, and at the same time, the vehicle may computethe camera's ego motion (rotation and translation). At each frame orimage, a short range model for the desired path is generated by thevehicle in a reference frame that is attached to the camera. The shortrange models may be stitched together to obtain a three dimensionalmodel of the road in some coordinate frame, which may be an arbitrary orpredetermined coordinate frame. The three dimensional model of the roadmay then be fitted by a spline, which may include or connect one or morepolynomials of suitable orders.

To conclude the short range road model at each frame, one or moredetection modules may be used. For example, a bottom-up lane detectionmodule may be used. The bottom-up lane detection module may be usefulwhen lane marks are drawn on the road. This module may look for edges inthe image and assembles them together to form the lane marks. A secondmodule may be used together with the bottom-up lane detection module.The second module is an end-to-end deep neural network, which may betrained to predict the correct short range path from an input image. Inboth modules, the road model may be detected in the image coordinateframe and transformed to a three dimensional space that may be virtuallyattached to the camera.

Although the reconstructed trajectory modeling method may introduce anaccumulation of errors due to the integration of ego motion over a longperiod of time, which may include a noise component, such errors may beinconsequential as the generated model may provide sufficient accuracyfor navigation over a local scale. In addition, it is possible to cancelthe integrated error by using external sources of information, such assatellite images or geodetic measurements. For example, the disclosedsystems and methods may use a GNSS receiver to cancel accumulatederrors. However, the GNSS positioning signals may not be alwaysavailable and accurate. The disclosed systems and methods may enable asteering application that depends weakly on the availability andaccuracy of GNSS positioning. In such systems, the usage of the GNSSsignals may be limited. For example, in some embodiments, the disclosedsystems may use the GNSS signals for database indexing purposes only.

In some embodiments, the range scale (e.g., local scale) that may berelevant for an autonomous vehicle navigation steering application maybe on the order of 50 meters, 100 meters, 200 meters, 300 meters, etc.Such distances may be used, as the geometrical road model is mainly usedfor two purposes: planning the trajectory ahead and localizing thevehicle on the road model. In some embodiments, the planning task mayuse the model over a typical range of 40 meters ahead (or any othersuitable distance ahead, such as 20 meters, 30 meters, 50 meters), whenthe control algorithm steers the vehicle according to a target pointlocated 1.3 seconds ahead (or any other time such as 1.5 seconds, 1.7seconds, 2 seconds, etc.). The localization task uses the road modelover a typical range of 60 meters behind the car (or any other suitabledistances, such as 50 meters, 100 meters, 150 meters, etc.), accordingto a method called “tail alignment” described in more detail in anothersection. The disclosed systems and methods may generate a geometricalmodel that has sufficient accuracy over particular range, such as 100meters, such that a planned trajectory will not deviate by more than,for example, 30 cm from the lane center.

As explained above, a three dimensional road model may be constructedfrom detecting short range sections and stitching them together. Thestitching may be enabled by computing a six degree ego motion model,using the videos and/or images captured by the camera, data from theinertial sensors that reflect the motions of the vehicle, and the hostvehicle velocity signal. The accumulated error may be small enough oversome local range scale, such as of the order of 100 meters. All this maybe completed in a single drive over a particular road segment.

In some embodiments, multiple drives may be used to average the resultedmodel, and to increase its accuracy further. The same car may travel thesame route multiple times, or multiple cars may send their collectedmodel data to a central server. In any case, a matching procedure may beperformed to identify overlapping models and to enable averaging inorder to generate target trajectories. The constructed model (e.g.,including the target trajectories) may be used for steering once aconvergence criterion is met. Subsequent drives may be used for furthermodel improvements and in order to accommodate infrastructure changes.

Sharing of driving experience (such as sensed data) between multiplecars becomes feasible if they are connected to a central server. Eachvehicle client may store a partial copy of a universal road model, whichmay be relevant for its current position. A bidirectional updateprocedure between the vehicles and the server may be performed by thevehicles and the server. The small footprint concept discussed aboveenables the disclosed systems and methods to perform the bidirectionalupdates using a very small bandwidth.

Information relating to potential landmarks may also be determined andforwarded to a central server. For example, the disclosed systems andmethods may determine one or more physical properties of a potentiallandmark based on one or more images that include the landmark. Thephysical properties may include a physical size (e.g., height, width) ofthe landmark, a distance from a vehicle to a landmark, a distancebetween the landmark to a previous landmark, the lateral position of thelandmark (e.g., the position of the landmark relative to the lane oftravel), the GPS coordinates of the landmark, a type of landmark,identification of text on the landmark, etc. For example, a vehicle mayanalyze one or more images captured by a camera to detect a potentiallandmark, such as a speed limit sign.

The vehicle may determine a distance from the vehicle to the landmark ora position associated with the landmark (e.g., any semantic ornon-semantic object or feature along a road segment) based on theanalysis of the one or more images. In some embodiments, the distancemay be determined based on analysis of images of the landmark using asuitable image analysis method, such as a scaling method and/or anoptical flow method. As previously noted, a position of theobject/feature may include a 2D image position (e.g., an X-Y pixelposition in one or more captured images) of one or more pointsassociated with the object/feature or may include a 3D real-worldposition of one or more points (e.g., determined through structure inmotion/optical flow techniques, LIDAR or RADAR information, etc.). Insome embodiments, the disclosed systems and methods may be configured todetermine a type or classification of a potential landmark. In case thevehicle determines that a certain potential landmark corresponds to apredetermined type or classification stored in a sparse map, it may besufficient for the vehicle to communicate to the server an indication ofthe type or classification of the landmark, along with its location. Theserver may store such indications. At a later time, during navigation, anavigating vehicle may capture an image that includes a representationof the landmark, process the image (e.g., using a classifier), andcompare the result landmark in order to confirm detection of the mappedlandmark and to use the mapped landmark in localizing the navigatingvehicle relative to the sparse map.

In some embodiments, multiple autonomous vehicles travelling on a roadsegment may communicate with a server. The vehicles (or clients) maygenerate a curve describing its drive (e.g., through ego motionintegration) in an arbitrary coordinate frame. The vehicles may detectlandmarks and locate them in the same frame. The vehicles may upload thecurve and the landmarks to the server. The server may collect data fromvehicles over multiple drives, and generate a unified road model. Forexample, as discussed below with respect to FIG. 19, the server maygenerate a sparse map having the unified road model using the uploadedcurves and landmarks.

The server may also distribute the model to clients (e.g., vehicles).For example, the server may distribute the sparse map to one or morevehicles. The server may continuously or periodically update the modelwhen receiving new data from the vehicles. For example, the server mayprocess the new data to evaluate whether the data includes informationthat should trigger an updated, or creation of new data on the server.The server may distribute the updated model or the updates to thevehicles for providing autonomous vehicle navigation.

The server may use one or more criteria for determining whether new datareceived from the vehicles should trigger an update to the model ortrigger creation of new data. For example, when the new data indicatesthat a previously recognized landmark at a specific location no longerexists, or is replaced by another landmark, the server may determinethat the new data should trigger an update to the model. As anotherexample, when the new data indicates that a road segment has beenclosed, and when this has been corroborated by data received from othervehicles, the server may determine that the new data should trigger anupdate to the model.

The server may distribute the updated model (or the updated portion ofthe model) to one or more vehicles that are traveling on the roadsegment, with which the updates to the model are associated. The servermay also distribute the updated model to vehicles that are about totravel on the road segment, or vehicles whose planned trip includes theroad segment, with which the updates to the model are associated. Forexample, while an autonomous vehicle is traveling along another roadsegment before reaching the road segment with which an update isassociated, the server may distribute the updates or updated model tothe autonomous vehicle before the vehicle reaches the road segment.

In some embodiments, the remote server may collect trajectories andlandmarks from multiple clients (e.g., vehicles that travel along acommon road segment). The server may match curves using landmarks andcreate an average road model based on the trajectories collected fromthe multiple vehicles. The server may also compute a graph of roads andthe most probable path at each node or conjunction of the road segment.For example, the remote server may align the trajectories to generate acrowdsourced sparse map from the collected trajectories.

The server may average landmark properties received from multiplevehicles that travelled along the common road segment, such as thedistances between one landmark to another (e.g., a previous one alongthe road segment) as measured by multiple vehicles, to determine anarc-length parameter and support localization along the path and speedcalibration for each client vehicle. The server may average the physicaldimensions of a landmark measured by multiple vehicles travelled alongthe common road segment and recognized the same landmark. The averagedphysical dimensions may be used to support distance estimation, such asthe distance from the vehicle to the landmark. The server may averagelateral positions of a landmark (e.g., position from the lane in whichvehicles are travelling in to the landmark) measured by multiplevehicles travelled along the common road segment and recognized the samelandmark. The averaged lateral portion may be used to support laneassignment. The server may average the GPS coordinates of the landmarkmeasured by multiple vehicles travelled along the same road segment andrecognized the same landmark. The averaged GPS coordinates of thelandmark may be used to support global localization or positioning ofthe landmark in the road model.

In some embodiments, the server may identify model changes, such asconstructions, detours, new signs, removal of signs, etc., based on datareceived from the vehicles. The server may continuously or periodicallyor instantaneously update the model upon receiving new data from thevehicles. The server may distribute updates to the model or the updatedmodel to vehicles for providing autonomous navigation. For example, asdiscussed further below, the server may use crowdsourced data to filterout “ghost” landmarks detected by vehicles.

In some embodiments, the server may analyze driver interventions duringthe autonomous driving. The server may analyze data received from thevehicle at the time and location where intervention occurs, and/or datareceived prior to the time the intervention occurred. The server mayidentify certain portions of the data that caused or are closely relatedto the intervention, for example, data indicating a temporary laneclosure setup, data indicating a pedestrian in the road. The server mayupdate the model based on the identified data. For example, the servermay modify one or more trajectories stored in the model.

FIG. 12 is a schematic illustration of a system that uses crowdsourcingto generate a sparse map (as well as distribute and navigate using acrowdsourced sparse map). FIG. 12 shows a road segment 1200 thatincludes one or more lanes. A plurality of vehicles 1205, 1210, 1215,1220, and 1225 may travel on road segment 1200 at the same time or atdifferent times (although shown as appearing on road segment 1200 at thesame time in FIG. 12). At least one of vehicles 1205, 1210, 1215, 1220,and 1225 may be an autonomous vehicle. For simplicity of the presentexample, all of the vehicles 1205, 1210, 1215, 1220, and 1225 arepresumed to be autonomous vehicles.

Each vehicle may be similar to vehicles disclosed in other embodiments(e.g., vehicle 200), and may include components or devices included inor associated with vehicles disclosed in other embodiments. Each vehiclemay be equipped with an image capture device or camera (e.g., imagecapture device 122 or camera 122). Each vehicle may communicate with aremote server 1230 via one or more networks (e.g., over a cellularnetwork and/or the Internet, etc.) through wireless communication paths1235, as indicated by the dashed lines. Each vehicle may transmit datato server 1230 and receive data from server 1230. For example, server1230 may collect data from multiple vehicles travelling on the roadsegment 1200 at different times, and may process the collected data togenerate an autonomous vehicle road navigation model, or an update tothe model. Server 1230 may transmit the autonomous vehicle roadnavigation model or the update to the model to the vehicles thattransmitted data to server 1230. Server 1230 may transmit the autonomousvehicle road navigation model or the update to the model to othervehicles that travel on road segment 1200 at later times.

As vehicles 1205, 1210, 1215, 1220, and 1225 travel on road segment1200, navigation information collected (e.g., detected, sensed, ormeasured) by vehicles 1205, 1210, 1215, 1220, and 1225 may betransmitted to server 1230. In some embodiments, the navigationinformation may be associated with the common road segment 1200. Thenavigation information may include a trajectory associated with each ofthe vehicles 1205, 1210, 1215, 1220, and 1225 as each vehicle travelsover road segment 1200. In some embodiments, the trajectory may bereconstructed based on data sensed by various sensors and devicesprovided on vehicle 1205. For example, the trajectory may bereconstructed based on at least one of accelerometer data, speed data,landmarks data, road geometry or profile data, vehicle positioning data,and ego motion data. In some embodiments, the trajectory may bereconstructed based on data from inertial sensors, such asaccelerometer, and the velocity of vehicle 1205 sensed by a speedsensor. In addition, in some embodiments, the trajectory may bedetermined (e.g., by a processor onboard each of vehicles 1205, 1210,1215, 1220, and 1225) based on sensed ego motion of the camera, whichmay indicate three dimensional translation and/or three dimensionalrotations (or rotational motions). The ego motion of the camera (andhence the vehicle body) may be determined from analysis of one or moreimages captured by the camera.

In some embodiments, the trajectory of vehicle 1205 may be determined bya processor provided aboard vehicle 1205 and transmitted to server 1230.In other embodiments, server 1230 may receive data sensed by the varioussensors and devices provided in vehicle 1205, and determine thetrajectory based on the data received from vehicle 1205.

In some embodiments, the navigation information transmitted fromvehicles 1205, 1210, 1215, 1220, and 1225 to server 1230 may includedata regarding the road surface, the road geometry, or the road profile.The geometry of road segment 1200 may include lane structure and/orlandmarks. The lane structure may include the total number of lanes ofroad segment 1200, the type of lanes (e.g., one-way lane, two-way lane,driving lane, passing lane, etc.), markings on lanes, width of lanes,etc. In some embodiments, the navigation information may include a laneassignment, e.g., which lane of a plurality of lanes a vehicle istraveling in. For example, the lane assignment may be associated with anumerical value “3” indicating that the vehicle is traveling on thethird lane from the left or right. As another example, the laneassignment may be associated with a text value “center lane” indicatingthe vehicle is traveling on the center lane.

Server 1230 may store the navigation information on a non-transitorycomputer-readable medium, such as a hard drive, a compact disc, a tape,a memory, etc. Server 1230 may generate (e.g., through a processorincluded in server 1230) at least a portion of an autonomous vehicleroad navigation model for the common road segment 1200 based on thenavigation information received from the plurality of vehicles 1205,1210, 1215, 1220, and 1225 and may store the model as a portion of asparse map. Server 1230 may determine a trajectory associated with eachlane based on crowdsourced data (e.g., navigation information) receivedfrom multiple vehicles (e.g., 1205, 1210, 1215, 1220, and 1225) thattravel on a lane of road segment at different times. Server 1230 maygenerate the autonomous vehicle road navigation model or a portion ofthe model (e.g., an updated portion) based on a plurality oftrajectories determined based on the crowd sourced navigation data.Server 1230 may transmit the model or the updated portion of the modelto one or more of autonomous vehicles 1205, 1210, 1215, 1220, and 1225traveling on road segment 1200 or any other autonomous vehicles thattravel on road segment at a later time for updating an existingautonomous vehicle road navigation model provided in a navigation systemof the vehicles. The autonomous vehicle road navigation model may beused by the autonomous vehicles in autonomously navigating along thecommon road segment 1200.

As explained above, the autonomous vehicle road navigation model may beincluded in a sparse map (e.g., sparse map 800 depicted in FIG. 8).Sparse map 800 may include sparse recording of data related to roadgeometry and/or landmarks along a road, which may provide sufficientinformation for guiding autonomous navigation of an autonomous vehicle,yet does not require excessive data storage. In some embodiments, theautonomous vehicle road navigation model may be stored separately fromsparse map 800, and may use map data from sparse map 800 when the modelis executed for navigation. In some embodiments, the autonomous vehicleroad navigation model may use map data included in sparse map 800 fordetermining target trajectories along road segment 1200 for guidingautonomous navigation of autonomous vehicles 1205, 1210, 1215, 1220, and1225 or other vehicles that later travel along road segment 1200. Forexample, when the autonomous vehicle road navigation model is executedby a processor included in a navigation system of vehicle 1205, themodel may cause the processor to compare the trajectories determinedbased on the navigation information received from vehicle 1205 withpredetermined trajectories included in sparse map 800 to validate and/orcorrect the current traveling course of vehicle 1205.

In the autonomous vehicle road navigation model, the geometry of a roadfeature or target trajectory may be encoded by a curve in athree-dimensional space. In one embodiment, the curve may be a threedimensional spline including one or more connecting three dimensionalpolynomials. As one of skill in the art would understand, a spline maybe a numerical function that is piece-wise defined by a series ofpolynomials for fitting data. A spline for fitting the three dimensionalgeometry data of the road may include a linear spline (first order), aquadratic spline (second order), a cubic spline (third order), or anyother splines (other orders), or a combination thereof. The spline mayinclude one or more three dimensional polynomials of different ordersconnecting (e.g., fitting) data points of the three dimensional geometrydata of the road. In some embodiments, the autonomous vehicle roadnavigation model may include a three dimensional spline corresponding toa target trajectory along a common road segment (e.g., road segment1200) or a lane of the road segment 1200.

As explained above, the autonomous vehicle road navigation modelincluded in the sparse map may include other information, such asidentification of at least one landmark along road segment 1200. Thelandmark may be visible within a field of view of a camera (e.g., camera122) installed on each of vehicles 1205, 1210, 1215, 1220, and 1225. Insome embodiments, camera 122 may capture an image of a landmark. Aprocessor (e.g., processor 180, 190, or processing unit 110) provided onvehicle 1205 may process the image of the landmark to extractidentification information for the landmark. The landmark identificationinformation, rather than an actual image of the landmark, may be storedin sparse map 800. The landmark identification information may requiremuch less storage space than an actual image. Other sensors or systems(e.g., GPS system) may also provide certain identification informationof the landmark (e.g., position of landmark). The landmark may includeat least one of a traffic sign, an arrow marking, a lane marking, adashed lane marking, a traffic light, a stop line, a directional sign(e.g., a highway exit sign with an arrow indicating a direction, ahighway sign with arrows pointing to different directions or places), alandmark beacon, or a lamppost. A landmark beacon refers to a device(e.g., an RFID device) installed along a road segment that transmits orreflects a signal to a receiver installed on a vehicle, such that whenthe vehicle passes by the device, the beacon received by the vehicle andthe location of the device (e.g., determined from GPS location of thedevice) may be used as a landmark to be included in the autonomousvehicle road navigation model and/or the sparse map 800.

The identification of at least one landmark may include a position ofthe at least one landmark. The position of the landmark may bedetermined based on position measurements performed using sensor systems(e.g., Global Positioning Systems, inertial based positioning systems,landmark beacon, etc.) associated with the plurality of vehicles 1205,1210, 1215, 1220, and 1225. In some embodiments, the position of thelandmark may be determined by averaging the position measurementsdetected, collected, or received by sensor systems on different vehicles1205, 1210, 1215, 1220, and 1225 through multiple drives. For example,vehicles 1205, 1210, 1215, 1220, and 1225 may transmit positionmeasurements data to server 1230, which may average the positionmeasurements and use the averaged position measurement as the positionof the landmark. The position of the landmark may be continuouslyrefined by measurements received from vehicles in subsequent drives.

The identification of the landmark may include a size of the landmark.The processor provided on a vehicle (e.g., 1205) may estimate thephysical size of the landmark based on the analysis of the images.Server 1230 may receive multiple estimates of the physical size of thesame landmark from different vehicles over different drives. Server 1230may average the different estimates to arrive at a physical size for thelandmark, and store that landmark size in the road model. The physicalsize estimate may be used to further determine or estimate a distancefrom the vehicle to the landmark. The distance to the landmark may beestimated based on the current speed of the vehicle and a scale ofexpansion based on the position of the landmark appearing in the imagesrelative to the focus of expansion of the camera. For example, thedistance to landmark may be estimated by Z=V*dt*R/D, where V is thespeed of vehicle, R is the distance in the image from the landmark attime t1 to the focus of expansion, and D is the change in distance forthe landmark in the image from t1 to t2. dt represents the (t2−t1). Forexample, the distance to landmark may be estimated by Z=V*dt*R/D, whereV is the speed of vehicle, R is the distance in the image between thelandmark and the focus of expansion, dt is a time interval, and D is theimage displacement of the landmark along the epipolar line. Otherequations equivalent to the above equation, such as Z=V*ω/Aω, may beused for estimating the distance to the landmark. Here, V is the vehiclespeed, w is an image length (like the object width), and Aw is thechange of that image length in a unit of time.

When the physical size of the landmark is known, the distance to thelandmark may also be determined based on the following equation:Z=f*W/ω, where f is the focal length, W is the size of the landmark(e.g., height or width), ω is the number of pixels when the landmarkleaves the image. From the above equation, a change in distance Z may becalculated using ΔZ=f*W*Δω/ω²+f*ΔW/ω, where ΔW decays to zero byaveraging, and where Δω is the number of pixels representing a boundingbox accuracy in the image. A value estimating the physical size of thelandmark may be calculated by averaging multiple observations at theserver side. The resulting error in distance estimation may be verysmall. There are two sources of error that may occur when using theformula above, namely ΔW and Δw. Their contribution to the distanceerror is given by ΔZ=f*W*Δω/ω²+f*ΔW/ω. However, ΔW decays to zero byaveraging; hence ΔZ is determined by Δω (e.g., the inaccuracy of thebounding box in the image).

For landmarks of unknown dimensions, the distance to the landmark may beestimated by tracking feature points on the landmark between successiveframes. For example, certain features appearing on a speed limit signmay be tracked between two or more image frames. Based on these trackedfeatures, a distance distribution per feature point may be generated.The distance estimate may be extracted from the distance distribution.For example, the most frequent distance appearing in the distancedistribution may be used as the distance estimate. As another example,the average of the distance distribution may be used as the distanceestimate.

FIG. 13 illustrates an example autonomous vehicle road navigation modelrepresented by a plurality of three dimensional splines 1301, 1302, and1303. The curves 1301, 1302, and 1303 shown in FIG. 13 are forillustration purpose only. Each spline may include one or more threedimensional polynomials connecting a plurality of data points 1310. Eachpolynomial may be a first order polynomial, a second order polynomial, athird order polynomial, or a combination of any suitable polynomialshaving different orders. Each data point 1310 may be associated with thenavigation information received from vehicles 1205, 1210, 1215, 1220,and 1225. In some embodiments, each data point 1310 may be associatedwith data related to landmarks (e.g., size, location, and identificationinformation of landmarks) and/or road signature profiles (e.g., roadgeometry, road roughness profile, road curvature profile, road widthprofile). In some embodiments, some data points 1310 may be associatedwith data related to landmarks, and others may be associated with datarelated to road signature profiles.

FIG. 14 illustrates raw location data 1410 (e.g., GPS data) receivedfrom five separate drives. One drive may be separate from another driveif it was traversed by separate vehicles at the same time, by the samevehicle at separate times, or by separate vehicles at separate times. Toaccount for errors in the location data 1410 and for differing locationsof vehicles within the same lane (e.g., one vehicle may drive closer tothe left of a lane than another), server 1230 may generate a mapskeleton 1420 using one or more statistical techniques to determinewhether variations in the raw location data 1410 represent actualdivergences or statistical errors. Each path within skeleton 1420 may belinked back to the raw data 1410 that formed the path. For example, thepath between A and B within skeleton 1420 is linked to raw data 1410from drives 2, 3, 4, and 5 but not from drive 1. Skeleton 1420 may notbe detailed enough to be used to navigate a vehicle (e.g., because itcombines drives from multiple lanes on the same road unlike the splinesdescribed above) but may provide useful topological information and maybe used to define intersections.

FIG. 15 illustrates an example by which additional detail may begenerated for a sparse map within a segment of a map skeleton (e.g.,segment A to B within skeleton 1420). As depicted in FIG. 15, the data(e.g. ego-motion data, road markings data, and the like) may be shown asa function of position S (or S₁ or S₂) along the drive. Server 1230 mayidentify landmarks for the sparse map by identifying unique matchesbetween landmarks 1501, 1503, and 1505 of drive 1510 and landmarks 1507and 1509 of drive 1520. Such a matching algorithm may result inidentification of landmarks 1511, 1513, and 1515. One skilled in the artwould recognize, however, that other matching algorithms may be used.For example, probability optimization may be used in lieu of or incombination with unique matching. Server 1230 may longitudinally alignthe drives to align the matched landmarks. For example, server 1230 mayselect one drive (e.g., drive 1520) as a reference drive and then shiftand/or elastically stretch the other drive(s) (e.g., drive 1510) foralignment.

FIG. 16 shows an example of aligned landmark data for use in a sparsemap. In the example of FIG. 16, landmark 1610 comprises a road sign. Theexample of FIG. 16 further depicts data from a plurality of drives 1601,1603, 1605, 1607, 1609, 1611, and 1613. In the example of FIG. 16, thedata from drive 1613 consists of a “ghost” landmark, and the server 1230may identify it as such because none of drives 1601, 1603, 1605, 1607,1609, and 1611 include an identification of a landmark in the vicinityof the identified landmark in drive 1613. Accordingly, server 1230 mayaccept potential landmarks when a ratio of images in which the landmarkdoes appear to images in which the landmark does not appear exceeds athreshold and/or may reject potential landmarks when a ratio of imagesin which the landmark does not appear to images in which the landmarkdoes appear exceeds a threshold.

FIG. 17 depicts a system 1700 for generating drive data, which may beused to crowdsource a sparse map. As depicted in FIG. 17, system 1700may include a camera 1701 and a locating device 1703 (e.g., a GPSlocator). Camera 1701 and locating device 1703 may be mounted on avehicle (e.g., one of vehicles 1205, 1210, 1215, 1220, and 1225). Camera1701 may produce a plurality of data of multiple types, e.g., ego motiondata, traffic sign data, road data, or the like. The camera data andlocation data may be segmented into drive segments 1705. For example,drive segments 1705 may each have camera data and location data fromless than 1 km of driving.

In some embodiments, system 1700 may remove redundancies in drivesegments 1705. For example, if a landmark appears in multiple imagesfrom camera 1701, system 1700 may strip the redundant data such that thedrive segments 1705 only contain one copy of the location of and anymetadata relating to the landmark. By way of further example, if a lanemarking appears in multiple images from camera 1701, system 1700 maystrip the redundant data such that the drive segments 1705 only containone copy of the location of and any metadata relating to the lanemarking.

System 1700 also includes a server (e.g., server 1230). Server 1230 mayreceive drive segments 1705 from the vehicle and recombine the drivesegments 1705 into a single drive 1707. Such an arrangement may allowfor reduce bandwidth requirements when transferring data between thevehicle and the server while also allowing for the server to store datarelating to an entire drive.

FIG. 18 depicts system 1700 of FIG. 17 further configured forcrowdsourcing a sparse map. As in FIG. 17, system 1700 includes vehicle1810, which captures drive data using, for example, a camera (whichproduces, e.g., ego motion data, traffic sign data, road data, or thelike) and a locating device (e.g., a GPS locator). As in FIG. 17,vehicle 1810 segments the collected data into drive segments (depictedas “DS1 1,” “DS2 1,” “DSN 1” in FIG. 18). Server 1230 then receives thedrive segments and reconstructs a drive (depicted as “Drive 1” in FIG.18) from the received segments.

As further depicted in FIG. 18, system 1700 also receives data fromadditional vehicles. For example, vehicle 1820 also captures drive datausing, for example, a camera (which produces, e.g., ego motion data,traffic sign data, road data, or the like) and a locating device (e.g.,a GPS locator). Similar to vehicle 1810, vehicle 1820 segments thecollected data into drive segments (depicted as “DS1 2,” “DS2 2,” “DSN2” in FIG. 18). Server 1230 then receives the drive segments andreconstructs a drive (depicted as “Drive 2” in FIG. 18) from thereceived segments. Any number of additional vehicles may be used. Forexample, FIG. 18 also includes “CAR N” that captures drive data,segments it into drive segments (depicted as “DS1 N,” “DS2 N,” “DSN N”in FIG. 18), and sends it to server 1230 for reconstruction into a drive(depicted as “Drive N” in FIG. 18).

As depicted in FIG. 18, server 1230 may construct a sparse map (depictedas “MAP”) using the reconstructed drives (e.g., “Drive 1,” “Drive 2,”and “Drive N”) collected from a plurality of vehicles (e.g., “CAR 1”(also labeled vehicle 1810), “CAR 2” (also labeled vehicle 1820), and“CAR N”).

FIG. 19 is a flowchart showing an example process 1900 for generating asparse map for autonomous vehicle navigation along a road segment.Process 1900 may be performed by one or more processing devices includedin server 1230.

Process 1900 may include receiving a plurality of images acquired as oneor more vehicles traverse the road segment (step 1905). Server 1230 mayreceive images from cameras included within one or more of vehicles1205, 1210, 1215, 1220, and 1225. For example, camera 122 may captureone or more images of the environment surrounding vehicle 1205 asvehicle 1205 travels along road segment 1200. In some embodiments,server 1230 may also receive stripped down image data that has hadredundancies removed by a processor on vehicle 1205, as discussed abovewith respect to FIG. 17.

Process 1900 may further include identifying, based on the plurality ofimages, at least one line representation of a road surface featureextending along the road segment (step 1910). Each line representationmay represent a path along the road segment substantially correspondingwith the road surface feature. For example, server 1230 may analyze theenvironmental images received from camera 122 to identify a road edge ora lane marking and determine a trajectory of travel along road segment1200 associated with the road edge or lane marking. In some embodiments,the trajectory (or line representation) may include a spline, apolynomial representation, or a curve. Server 1230 may determine thetrajectory of travel of vehicle 1205 based on camera ego motions (e.g.,three dimensional translation and/or three dimensional rotationalmotions) received at step 1905.

Process 1900 may also include identifying, based on the plurality ofimages, a plurality of landmarks associated with the road segment (step1910). For example, server 1230 may analyze the environmental imagesreceived from camera 122 to identify one or more landmarks, such as roadsign along road segment 1200. Server 1230 may identify the landmarksusing analysis of the plurality of images acquired as one or morevehicles traverse the road segment. To enable crowdsourcing, theanalysis may include rules regarding accepting and rejecting possiblelandmarks associated with the road segment. For example, the analysismay include accepting potential landmarks when a ratio of images inwhich the landmark does appear to images in which the landmark does notappear exceeds a threshold and/or rejecting potential landmarks when aratio of images in which the landmark does not appear to images in whichthe landmark does appear exceeds a threshold.

Process 1900 may include other operations or steps performed by server1230. For example, the navigation information may include a targettrajectory for vehicles to travel along a road segment, and process 1900may include clustering, by server 1230, vehicle trajectories related tomultiple vehicles travelling on the road segment and determining thetarget trajectory based on the clustered vehicle trajectories, asdiscussed in further detail below. Clustering vehicle trajectories mayinclude clustering, by server 1230, the multiple trajectories related tothe vehicles travelling on the road segment into a plurality of clustersbased on at least one of the absolute heading of vehicles or laneassignment of the vehicles. Generating the target trajectory may includeaveraging, by server 1230, the clustered trajectories. By way of furtherexample, process 1900 may include aligning data received in step 1905.Other processes or steps performed by server 1230, as described above,may also be included in process 1900.

The disclosed systems and methods may include other features. Forexample, the disclosed systems may use local coordinates, rather thanglobal coordinates. For autonomous driving, some systems may presentdata in world coordinates. For example, longitude and latitudecoordinates on the earth surface may be used. In order to use the mapfor steering, the host vehicle may determine its position andorientation relative to the map. It seems natural to use a GPS device onboard, in order to position the vehicle on the map and in order to findthe rotation transformation between the body reference frame and theworld reference frame (e.g., North, East and Down). Once the bodyreference frame is aligned with the map reference frame, then thedesired route may be expressed in the body reference frame and thesteering commands may be computed or generated.

The disclosed systems and methods may enable autonomous vehiclenavigation (e.g., steering control) with low footprint models, which maybe collected by the autonomous vehicles themselves without the aid ofexpensive surveying equipment. To support the autonomous navigation(e.g., steering applications), the road model may include a sparse maphaving the geometry of the road, its lane structure, and landmarks thatmay be used to determine the location or position of vehicles along atrajectory included in the model. As discussed above, generation of thesparse map may be performed by a remote server that communicates withvehicles travelling on the road and that receives data from thevehicles. The data may include sensed data, trajectories reconstructedbased on the sensed data, and/or recommended trajectories that mayrepresent modified reconstructed trajectories. As discussed below, theserver may transmit the model back to the vehicles or other vehiclesthat later travel on the road to aid in autonomous navigation.

FIG. 20 illustrates a block diagram of server 1230. Server 1230 mayinclude a communication unit 2005, which may include both hardwarecomponents (e.g., communication control circuits, switches, andantenna), and software components (e.g., communication protocols,computer codes). For example, communication unit 2005 may include atleast one network interface. Server 1230 may communicate with vehicles1205, 1210, 1215, 1220, and 1225 through communication unit 2005. Forexample, server 1230 may receive, through communication unit 2005,navigation information transmitted from vehicles 1205, 1210, 1215, 1220,and 1225. Server 1230 may distribute, through communication unit 2005,the autonomous vehicle road navigation model to one or more autonomousvehicles.

Server 1230 may include at least one non-transitory storage medium 2010,such as a hard drive, a compact disc, a tape, etc. Storage device 1410may be configured to store data, such as navigation information receivedfrom vehicles 1205, 1210, 1215, 1220, and 1225 and/or the autonomousvehicle road navigation model that server 1230 generates based on thenavigation information. Storage device 2010 may be configured to storeany other information, such as a sparse map (e.g., sparse map 800discussed above with respect to FIG. 8).

In addition to or in place of storage device 2010, server 1230 mayinclude a memory 2015. Memory 2015 may be similar to or different frommemory 140 or 150. Memory 2015 may be a non-transitory memory, such as aflash memory, a random access memory, etc. Memory 2015 may be configuredto store data, such as computer codes or instructions executable by aprocessor (e.g., processor 2020), map data (e.g., data of sparse map800), the autonomous vehicle road navigation model, and/or navigationinformation received from vehicles 1205, 1210, 1215, 1220, and 1225.

Server 1230 may include at least one processing device 2020 configuredto execute computer codes or instructions stored in memory 2015 toperform various functions. For example, processing device 2020 mayanalyze the navigation information received from vehicles 1205, 1210,1215, 1220, and 1225, and generate the autonomous vehicle roadnavigation model based on the analysis. Processing device 2020 maycontrol communication unit 1405 to distribute the autonomous vehicleroad navigation model to one or more autonomous vehicles (e.g., one ormore of vehicles 1205, 1210, 1215, 1220, and 1225 or any vehicle thattravels on road segment 1200 at a later time). Processing device 2020may be similar to or different from processor 180, 190, or processingunit 110.

FIG. 21 illustrates a block diagram of memory 2015, which may storecomputer code or instructions for performing one or more operations forgenerating a road navigation model for use in autonomous vehiclenavigation. As shown in FIG. 21, memory 2015 may store one or moremodules for performing the operations for processing vehicle navigationinformation. For example, memory 2015 may include a model generatingmodule 2105 and a model distributing module 2110. Processor 2020 mayexecute the instructions stored in any of modules 2105 and 2110 includedin memory 2015.

Model generating module 2105 may store instructions which, when executedby processor 2020, may generate at least a portion of an autonomousvehicle road navigation model for a common road segment (e.g., roadsegment 1200) based on navigation information received from vehicles1205, 1210, 1215, 1220, and 1225. For example, in generating theautonomous vehicle road navigation model, processor 2020 may clustervehicle trajectories along the common road segment 1200 into differentclusters. Processor 2020 may determine a target trajectory along thecommon road segment 1200 based on the clustered vehicle trajectories foreach of the different clusters. Such an operation may include finding amean or average trajectory of the clustered vehicle trajectories (e.g.,by averaging data representing the clustered vehicle trajectories) ineach cluster. In some embodiments, the target trajectory may beassociated with a single lane of the common road segment 1200.

The road model and/or sparse map may store trajectories associated witha road segment. These trajectories may be referred to as targettrajectories, which are provided to autonomous vehicles for autonomousnavigation. The target trajectories may be received from multiplevehicles, or may be generated based on actual trajectories orrecommended trajectories (actual trajectories with some modifications)received from multiple vehicles. The target trajectories included in theroad model or sparse map may be continuously updated (e.g., averaged)with new trajectories received from other vehicles.

Vehicles travelling on a road segment may collect data by varioussensors. The data may include landmarks, road signature profile, vehiclemotion (e.g., accelerometer data, speed data), vehicle position (e.g.,GPS data), and may either reconstruct the actual trajectoriesthemselves, or transmit the data to a server, which will reconstruct theactual trajectories for the vehicles. In some embodiments, the vehiclesmay transmit data relating to a trajectory (e.g., a curve in anarbitrary reference frame), landmarks data, and lane assignment alongtraveling path to server 1230. Various vehicles travelling along thesame road segment at multiple drives may have different trajectories.Server 1230 may identify routes or trajectories associated with eachlane from the trajectories received from vehicles through a clusteringprocess.

FIG. 22 illustrates a process of clustering vehicle trajectoriesassociated with vehicles 1205, 1210, 1215, 1220, and 1225 fordetermining a target trajectory for the common road segment (e.g., roadsegment 1200). The target trajectory or a plurality of targettrajectories determined from the clustering process may be included inthe autonomous vehicle road navigation model or sparse map 800. In someembodiments, vehicles 1205, 1210, 1215, 1220, and 1225 traveling alongroad segment 1200 may transmit a plurality of trajectories 2200 toserver 1230. In some embodiments, server 1230 may generate trajectoriesbased on landmark, road geometry, and vehicle motion informationreceived from vehicles 1205, 1210, 1215, 1220, and 1225. To generate theautonomous vehicle road navigation model, server 1230 may clustervehicle trajectories 1600 into a plurality of clusters 2205, 2210, 2215,2220, 2225, and 2230, as shown in FIG. 22.

Clustering may be performed using various criteria. In some embodiments,all drives in a cluster may be similar with respect to the absoluteheading along the road segment 1200. The absolute heading may beobtained from GPS signals received by vehicles 1205, 1210, 1215, 1220,and 1225. In some embodiments, the absolute heading may be obtainedusing dead reckoning Dead reckoning, as one of skill in the art wouldunderstand, may be used to determine the current position and henceheading of vehicles 1205, 1210, 1215, 1220, and 1225 by using previouslydetermined position, estimated speed, etc. Trajectories clustered byabsolute heading may be useful for identifying routes along theroadways.

In some embodiments, all the drives in a cluster may be similar withrespect to the lane assignment (e.g., in the same lane before and aftera junction) along the drive on road segment 1200. Trajectories clusteredby lane assignment may be useful for identifying lanes along theroadways. In some embodiments, both criteria (e.g., absolute heading andlane assignment) may be used for clustering.

In each cluster 2205, 2210, 2215, 2220, 2225, and 2230, trajectories maybe averaged to obtain a target trajectory associated with the specificcluster. For example, the trajectories from multiple drives associatedwith the same lane cluster may be averaged. The averaged trajectory maybe a target trajectory associate with a specific lane. To average acluster of trajectories, server 1230 may select a reference frame of anarbitrary trajectory C0. For all other trajectories (C1, Cn), server1230 may find a rigid transformation that maps Ci to C0, where i=1, 2, .. . , n, where n is a positive integer number, corresponding to thetotal number of trajectories included in the cluster. Server 1230 maycompute a mean curve or trajectory in the C0 reference frame.

In some embodiments, the landmarks may define an arc length matchingbetween different drives, which may be used for alignment oftrajectories with lanes. In some embodiments, lane marks before andafter a junction may be used for alignment of trajectories with lanes.

To assemble lanes from the trajectories, server 1230 may select areference frame of an arbitrary lane. Server 1230 may map partiallyoverlapping lanes to the selected reference frame. Server 1230 maycontinue mapping until all lanes are in the same reference frame. Lanesthat are next to each other may be aligned as if they were the samelane, and later they may be shifted laterally.

Landmarks recognized along the road segment may be mapped to the commonreference frame, first at the lane level, then at the junction level.For example, the same landmarks may be recognized multiple times bymultiple vehicles in multiple drives. The data regarding the samelandmarks received in different drives may be slightly different. Suchdata may be averaged and mapped to the same reference frame, such as theC0 reference frame. Additionally or alternatively, the variance of thedata of the same landmark received in multiple drives may be calculated.

In some embodiments, each lane of road segment 120 may be associatedwith a target trajectory and certain landmarks. The target trajectory ora plurality of such target trajectories may be included in theautonomous vehicle road navigation model, which may be used later byother autonomous vehicles travelling along the same road segment 1200.Landmarks identified by vehicles 1205, 1210, 1215, 1220, and 1225 whilethe vehicles travel along road segment 1200 may be recorded inassociation with the target trajectory. The data of the targettrajectories and landmarks may be continuously or periodically updatedwith new data received from other vehicles in subsequent drives.

For localization of an autonomous vehicle, the disclosed systems andmethods may use an Extended Kalman Filter. The location of the vehiclemay be determined based on three dimensional position data and/or threedimensional orientation data, prediction of future location ahead ofvehicle's current location by integration of ego motion. Thelocalization of vehicle may be corrected or adjusted by imageobservations of landmarks. For example, when vehicle detects a landmarkwithin an image captured by the camera, the landmark may be compared toa known landmark stored within the road model or sparse map 800. Theknown landmark may have a known location (e.g., GPS data) along a targettrajectory stored in the road model and/or sparse map 800. Based on thecurrent speed and images of the landmark, the distance from the vehicleto the landmark may be estimated. The location of the vehicle along atarget trajectory may be adjusted based on the distance to the landmarkand the landmark's known location (stored in the road model or sparsemap 800). The landmark's position/location data (e.g., mean values frommultiple drives) stored in the road model and/or sparse map 800 may bepresumed to be accurate.

In some embodiments, the disclosed system may form a closed loopsubsystem, in which estimation of the vehicle six degrees of freedomlocation (e.g., three dimensional position data plus three dimensionalorientation data) may be used for navigating (e.g., steering the wheelof) the autonomous vehicle to reach a desired point (e.g., 1.3 secondahead in the stored). In turn, data measured from the steering andactual navigation may be used to estimate the six degrees of freedomlocation.

In some embodiments, poles along a road, such as lampposts and power orcable line poles may be used as landmarks for localizing the vehicles.Other landmarks such as traffic signs, traffic lights, arrows on theroad, stop lines, as well as static features or signatures of an objectalong the road segment may also be used as landmarks for localizing thevehicle. When poles are used for localization, the x observation of thepoles (i.e., the viewing angle from the vehicle) may be used, ratherthan the y observation (i.e., the distance to the pole) since thebottoms of the poles may be occluded and sometimes they are not on theroad plane.

FIG. 23 illustrates a navigation system for a vehicle, which may be usedfor autonomous navigation using a crowdsourced sparse map. Forillustration, the vehicle is referenced as vehicle 1205. The vehicleshown in FIG. 23 may be any other vehicle disclosed herein, including,for example, vehicles 1210, 1215, 1220, and 1225, as well as vehicle 200shown in other embodiments. As shown in FIG. 12, vehicle 1205 maycommunicate with server 1230. Vehicle 1205 may include an image capturedevice 122 (e.g., camera 122). Vehicle 1205 may include a navigationsystem 2300 configured for providing navigation guidance for vehicle1205 to travel on a road (e.g., road segment 1200). Vehicle 1205 mayalso include other sensors, such as a speed sensor 2320 and anaccelerometer 2325. Speed sensor 2320 may be configured to detect thespeed of vehicle 1205. Accelerometer 2325 may be configured to detect anacceleration or deceleration of vehicle 1205. Vehicle 1205 shown in FIG.23 may be an autonomous vehicle, and the navigation system 2300 may beused for providing navigation guidance for autonomous driving.Alternatively, vehicle 1205 may also be a non-autonomous,human-controlled vehicle, and navigation system 2300 may still be usedfor providing navigation guidance.

Navigation system 2300 may include a communication unit 2305 configuredto communicate with server 1230 through communication path 1235.Navigation system 2300 may also include a GPS unit 2310 configured toreceive and process GPS signals. Navigation system 2300 may furtherinclude at least one processor 2315 configured to process data, such asGPS signals, map data from sparse map 800 (which may be stored on astorage device provided onboard vehicle 1205 and/or received from server1230), road geometry sensed by a road profile sensor 2330, imagescaptured by camera 122, and/or autonomous vehicle road navigation modelreceived from server 1230. The road profile sensor 2330 may includedifferent types of devices for measuring different types of roadprofile, such as road surface roughness, road width, road elevation,road curvature, etc. For example, the road profile sensor 2330 mayinclude a device that measures the motion of a suspension of vehicle2305 to derive the road roughness profile. In some embodiments, the roadprofile sensor 2330 may include radar sensors to measure the distancefrom vehicle 1205 to road sides (e.g., barrier on the road sides),thereby measuring the width of the road. In some embodiments, the roadprofile sensor 2330 may include a device configured for measuring the upand down elevation of the road. In some embodiment, the road profilesensor 2330 may include a device configured to measure the roadcurvature. For example, a camera (e.g., camera 122 or another camera)may be used to capture images of the road showing road curvatures.Vehicle 1205 may use such images to detect road curvatures.

The at least one processor 2315 may be programmed to receive, fromcamera 122, at least one environmental image associated with vehicle1205. The at least one processor 2315 may analyze the at least oneenvironmental image to determine navigation information related to thevehicle 1205. The navigation information may include a trajectoryrelated to the travel of vehicle 1205 along road segment 1200. The atleast one processor 2315 may determine the trajectory based on motionsof camera 122 (and hence the vehicle), such as three dimensionaltranslation and three dimensional rotational motions. In someembodiments, the at least one processor 2315 may determine thetranslation and rotational motions of camera 122 based on analysis of aplurality of images acquired by camera 122. In some embodiments, thenavigation information may include lane assignment information (e.g., inwhich lane vehicle 1205 is travelling along road segment 1200). Thenavigation information transmitted from vehicle 1205 to server 1230 maybe used by server 1230 to generate and/or update an autonomous vehicleroad navigation model, which may be transmitted back from server 1230 tovehicle 1205 for providing autonomous navigation guidance for vehicle1205.

The at least one processor 2315 may also be programmed to transmit thenavigation information from vehicle 1205 to server 1230. In someembodiments, the navigation information may be transmitted to server1230 along with road information. The road location information mayinclude at least one of the GPS signal received by the GPS unit 2310,landmark information, road geometry, lane information, etc. The at leastone processor 2315 may receive, from server 1230, the autonomous vehicleroad navigation model or a portion of the model. The autonomous vehicleroad navigation model received from server 1230 may include at least oneupdate based on the navigation information transmitted from vehicle 1205to server 1230. The portion of the model transmitted from server 1230 tovehicle 1205 may include an updated portion of the model. The at leastone processor 2315 may cause at least one navigational maneuver (e.g.,steering such as making a turn, braking, accelerating, passing anothervehicle, etc.) by vehicle 1205 based on the received autonomous vehicleroad navigation model or the updated portion of the model.

The at least one processor 2315 may be configured to communicate withvarious sensors and components included in vehicle 1205, includingcommunication unit 1705, GPS unit 2315, camera 122, speed sensor 2320,accelerometer 2325, and road profile sensor 2330. The at least oneprocessor 2315 may collect information or data from various sensors andcomponents, and transmit the information or data to server 1230 throughcommunication unit 2305. Alternatively or additionally, various sensorsor components of vehicle 1205 may also communicate with server 1230 andtransmit data or information collected by the sensors or components toserver 1230.

In some embodiments, vehicles 1205, 1210, 1215, 1220, and 1225 maycommunicate with each other, and may share navigation information witheach other, such that at least one of the vehicles 1205, 1210, 1215,1220, and 1225 may generate the autonomous vehicle road navigation modelusing crowdsourcing, e.g., based on information shared by othervehicles. In some embodiments, vehicles 1205, 1210, 1215, 1220, and 1225may share navigation information with each other and each vehicle mayupdate its own the autonomous vehicle road navigation model provided inthe vehicle. In some embodiments, at least one of the vehicles 1205,1210, 1215, 1220, and 1225 (e.g., vehicle 1205) may function as a hubvehicle. The at least one processor 2315 of the hub vehicle (e.g.,vehicle 1205) may perform some or all of the functions performed byserver 1230. For example, the at least one processor 2315 of the hubvehicle may communicate with other vehicles and receive navigationinformation from other vehicles. The at least one processor 2315 of thehub vehicle may generate the autonomous vehicle road navigation model oran update to the model based on the shared information received fromother vehicles. The at least one processor 2315 of the hub vehicle maytransmit the autonomous vehicle road navigation model or the update tothe model to other vehicles for providing autonomous navigationguidance.

Navigation Based on Sparse Maps

As previously discussed, the autonomous vehicle road navigation modelincluding sparse map 800 may include a plurality of mapped lane marksand a plurality of mapped objects/features associated with a roadsegment. As discussed in greater detail below, these mapped lane marks,objects, and features may be used when the autonomous vehicle navigates.For example, in some embodiments, the mapped objects and features may beused to localized a host vehicle relative to the map (e.g., relative toa mapped target trajectory). The mapped lane marks may be used (e.g., asa check) to determine a lateral position and/or orientation relative toa planned or target trajectory. With this position information, theautonomous vehicle may be able to adjust a heading direction to match adirection of a target trajectory at the determined position.

Vehicle 200 may be configured to detect lane marks in a given roadsegment. The road segment may include any markings on a road for guidingvehicle traffic on a roadway. For example, the lane marks may becontinuous or dashed lines demarking the edge of a lane of travel. Thelane marks may also include double lines, such as a double continuouslines, double dashed lines or a combination of continuous and dashedlines indicating, for example, whether passing is permitted in anadjacent lane. The lane marks may also include freeway entrance and exitmarkings indicating, for example, a deceleration lane for an exit rampor dotted lines indicating that a lane is turn-only or that the lane isending. The markings may further indicate a work zone, a temporary laneshift, a path of travel through an intersection, a median, a specialpurpose lane (e.g., a bike lane, HOV lane, etc.), or other miscellaneousmarkings (e.g., crosswalk, a speed hump, a railway crossing, a stopline, etc.).

Vehicle 200 may use cameras, such as image capture devices 122 and 124included in image acquisition unit 120, to capture images of thesurrounding lane marks. Vehicle 200 may analyze the images to detectpoint locations associated with the lane marks based on featuresidentified within one or more of the captured images. These pointlocations may be uploaded to a server to represent the lane marks insparse map 800. Depending on the position and field of view of thecamera, lane marks may be detected for both sides of the vehiclesimultaneously from a single image. In other embodiments, differentcameras may be used to capture images on multiple sides of the vehicle.Rather than uploading actual images of the lane marks, the marks may bestored in sparse map 800 as a spline or a series of points, thusreducing the size of sparse map 800 and/or the data that must beuploaded remotely by the vehicle.

FIGS. 24A-24D illustrate exemplary point locations that may be detectedby vehicle 200 to represent particular lane marks. Similar to thelandmarks described above, vehicle 200 may use various image recognitionalgorithms or software to identify point locations within a capturedimage. For example, vehicle 200 may recognize a series of edge points,corner points or various other point locations associated with aparticular lane mark. FIG. 24A shows a continuous lane mark 2410 thatmay be detected by vehicle 200. Lane mark 2410 may represent the outsideedge of a roadway, represented by a continuous white line. As shown inFIG. 24A, vehicle 200 may be configured to detect a plurality of edgelocation points 2411 along the lane mark. Location points 2411 may becollected to represent the lane mark at any intervals sufficient tocreate a mapped lane mark in the sparse map. For example, the lane markmay be represented by one point per meter of the detected edge, onepoint per every five meters of the detected edge, or at other suitablespacings. In some embodiments, the spacing may be determined by otherfactors, rather than at set intervals such as, for example, based onpoints where vehicle 200 has a highest confidence ranking of thelocation of the detected points. Although FIG. 24A shows edge locationpoints on an interior edge of lane mark 2410, points may be collected onthe outside edge of the line or along both edges. Further, while asingle line is shown in FIG. 24A, similar edge points may be detectedfor a double continuous line. For example, points 2411 may be detectedalong an edge of one or both of the continuous lines.

Vehicle 200 may also represent lane marks differently depending on thetype or shape of lane mark. FIG. 24B shows an exemplary dashed lane mark2420 that may be detected by vehicle 200. Rather than identifying edgepoints, as in FIG. 24A, vehicle may detect a series of corner points2421 representing corners of the lane dashes to define the full boundaryof the dash. While FIG. 24B shows each corner of a given dash markingbeing located, vehicle 200 may detect or upload a subset of the pointsshown in the figure. For example, vehicle 200 may detect the leadingedge or leading corner of a given dash mark, or may detect the twocorner points nearest the interior of the lane. Further, not every dashmark may be captured, for example, vehicle 200 may capture and/or recordpoints representing a sample of dash marks (e.g., every other, everythird, every fifth, etc.) or dash marks at a predefined spacing (e.g.,every meter, every five meters, every 10 meters, etc.) Corner points mayalso be detected for similar lane marks, such as markings showing a laneis for an exit ramp, that a particular lane is ending, or other variouslane marks that may have detectable corner points. Corner points mayalso be detected for lane marks consisting of double dashed lines or acombination of continuous and dashed lines.

In some embodiments, the points uploaded to the server to generate themapped lane marks may represent other points besides the detected edgepoints or corner points. FIG. 24C illustrates a series of points thatmay represent a centerline of a given lane mark. For example, continuouslane 2410 may be represented by centerline points 2441 along acenterline 2440 of the lane mark. In some embodiments, vehicle 200 maybe configured to detect these center points using various imagerecognition techniques, such as convolutional neural networks (CNN),scale-invariant feature transform (SIFT), histogram of orientedgradients (HOG) features, or other techniques. Alternatively, vehicle200 may detect other points, such as edge points 2411 shown in FIG. 24A,and may calculate centerline points 2441, for example, by detectingpoints along each edge and determining a midpoint between the edgepoints. Similarly, dashed lane mark 2420 may be represented bycenterline points 2451 along a centerline 2450 of the lane mark. Thecenterline points may be located at the edge of a dash, as shown in FIG.24C, or at various other locations along the centerline. For example,each dash may be represented by a single point in the geometric centerof the dash. The points may also be spaced at a predetermined intervalalong the centerline (e.g., every meter, 5 meters, 10 meters, etc.). Thecenterline points 2451 may be detected directly by vehicle 200, or maybe calculated based on other detected reference points, such as cornerpoints 2421, as shown in FIG. 24B. A centerline may also be used torepresent other lane mark types, such as a double line, using similartechniques as above.

In some embodiments, vehicle 200 may identify points representing otherfeatures, such as a vertex between two intersecting lane marks. FIG. 24Dshows exemplary points representing an intersection between two lanemarks 2460 and 2465. Vehicle 200 may calculate a vertex point 2466representing an intersection between the two lane marks. For example,one of lane marks 2460 or 2465 may represent a train crossing area orother crossing area in the road segment. While lane marks 2460 and 2465are shown as crossing each other perpendicularly, various otherconfigurations may be detected. For example, the lane marks 2460 and2465 may cross at other angles, or one or both of the lane marks mayterminate at the vertex point 2466. Similar techniques may also beapplied for intersections between dashed or other lane mark types. Inaddition to vertex point 2466, various other points 2467 may also bedetected, providing further information about the orientation of lanemarks 2460 and 2465.

Vehicle 200 may associate real-world coordinates with each detectedpoint of the lane mark. For example, location identifiers may begenerated, including coordinate for each point, to upload to a serverfor mapping the lane mark. The location identifiers may further includeother identifying information about the points, including whether thepoint represents a corner point, an edge point, center point, etc.Vehicle 200 may therefore be configured to determine a real-worldposition of each point based on analysis of the images. For example,vehicle 200 may detect other features in the image, such as the variouslandmarks described above, to locate the real-world position of the lanemarks. This may involve determining the location of the lane marks inthe image relative to the detected landmark or determining the positionof the vehicle based on the detected landmark and then determining adistance from the vehicle (or target trajectory of the vehicle) to thelane mark. When a landmark is not available, the location of the lanemark points may be determined relative to a position of the vehicledetermined based on dead reckoning. The real-world coordinates includedin the location identifiers may be represented as absolute coordinates(e.g., latitude/longitude coordinates), or may be relative to otherfeatures, such as based on a longitudinal position along a targettrajectory and a lateral distance from the target trajectory. Thelocation identifiers may then be uploaded to a server for generation ofthe mapped lane marks in the navigation model (such as sparse map 800).In some embodiments, the server may construct a spline representing thelane marks of a road segment. Alternatively, vehicle 200 may generatethe spline and upload it to the server to be recorded in thenavigational model.

FIG. 24E shows an exemplary navigation model or sparse map for acorresponding road segment that includes mapped lane marks. The sparsemap may include a target trajectory 2475 for a vehicle to follow along aroad segment. As described above, target trajectory 2475 may representan ideal path for a vehicle to take as it travels the corresponding roadsegment, or may be located elsewhere on the road (e.g., a centerline ofthe road, etc.). Target trajectory 2475 may be calculated in the variousmethods described above, for example, based on an aggregation (e.g., aweighted combination) of two or more reconstructed trajectories ofvehicles traversing the same road segment.

In some embodiments, the target trajectory may be generated equally forall vehicle types and for all road, vehicle, and/or environmentconditions. In other embodiments, however, various other factors orvariables may also be considered in generating the target trajectory. Adifferent target trajectory may be generated for different types ofvehicles (e.g., a private car, a light truck, and a full trailer). Forexample, a target trajectory with relatively tighter turning radii maybe generated for a small private car than a larger semi-trailer truck.In some embodiments, road, vehicle and environmental conditions may beconsidered as well. For example, a different target trajectory may begenerated for different road conditions (e.g., wet, snowy, icy, dry,etc.), vehicle conditions (e.g., tire condition or estimated tirecondition, brake condition or estimated brake condition, amount of fuelremaining, etc.) or environmental factors (e.g., time of day,visibility, weather, etc.). The target trajectory may also depend on oneor more aspects or features of a particular road segment (e.g., speedlimit, frequency and size of turns, grade, etc.). In some embodiments,various user settings may also be used to determine the targettrajectory, such as a set driving mode (e.g., desired drivingaggressiveness, economy mode, etc.).

The sparse map may also include mapped lane marks 2470 and 2480representing lane marks along the road segment. The mapped lane marksmay be represented by a plurality of location identifiers 2471 and 2481.As described above, the location identifiers may include locations inreal world coordinates of points associated with a detected lane mark.Similar to the target trajectory in the model, the lane marks may alsoinclude elevation data and may be represented as a curve inthree-dimensional space. For example, the curve may be a splineconnecting three dimensional polynomials of suitable order the curve maybe calculated based on the location identifiers. The mapped lane marksmay also include other information or metadata about the lane mark, suchas an identifier of the type of lane mark (e.g., between two lanes withthe same direction of travel, between two lanes of opposite direction oftravel, edge of a roadway, etc.) and/or other characteristics of thelane mark (e.g., continuous, dashed, single line, double line, yellow,white, etc.). In some embodiments, the mapped lane marks may becontinuously updated within the model, for example, using crowdsourcingtechniques. The same vehicle may upload location identifiers duringmultiple occasions of travelling the same road segment or data may beselected from a plurality of vehicles (such as 1205, 1210, 1215, 1220,and 1225) travelling the road segment at different times. Sparse map 800may then be updated or refined based on subsequent location identifiersreceived from the vehicles and stored in the system. As the mapped lanemarks are updated and refined, the updated road navigation model and/orsparse map may be distributed to a plurality of autonomous vehicles.

Generating the mapped lane marks in the sparse map may also includedetecting and/or mitigating errors based on anomalies in the images orin the actual lane marks themselves. FIG. 24F shows an exemplary anomaly2495 associated with detecting a lane mark 2490. Anomaly 2495 may appearin the image captured by vehicle 200, for example, from an objectobstructing the camera's view of the lane mark, debris on the lens, etc.In some instances, the anomaly may be due to the lane mark itself, whichmay be damaged or worn away, or partially covered, for example, by dirt,debris, water, snow or other materials on the road. Anomaly 2495 mayresult in an erroneous point 2491 being detected by vehicle 200. Sparsemap 800 may provide the correct the mapped lane mark and exclude theerror. In some embodiments, vehicle 200 may detect erroneous point 2491for example, by detecting anomaly 2495 in the image, or by identifyingthe error based on detected lane mark points before and after theanomaly. Based on detecting the anomaly, the vehicle may omit point 2491or may adjust it to be in line with other detected points. In otherembodiments, the error may be corrected after the point has beenuploaded, for example, by determining the point is outside of anexpected threshold based on other points uploaded during the same trip,or based on an aggregation of data from previous trips along the sameroad segment.

The mapped lane marks in the navigation model and/or sparse map may alsobe used for navigation by an autonomous vehicle traversing thecorresponding roadway. For example, a vehicle navigating along a targettrajectory may periodically use the mapped lane marks in the sparse mapto align itself with the target trajectory. As mentioned above, betweenlandmarks the vehicle may navigate based on dead reckoning in which thevehicle uses sensors to determine its ego motion and estimate itsposition relative to the target trajectory. Errors may accumulate overtime and vehicle's position determinations relative to the targettrajectory may become increasingly less accurate. Accordingly, thevehicle may use lane marks occurring in sparse map 800 (and their knownlocations) to reduce the dead reckoning-induced errors in positiondetermination. In this way, the identified lane marks included in sparsemap 800 may serve as navigational anchors from which an accurateposition of the vehicle relative to a target trajectory may bedetermined.

FIG. 25A shows an exemplary image 2500 of a vehicle's surroundingenvironment that may be used for navigation based on the mapped lanemarks. Image 2500 may be captured, for example, by vehicle 200 throughimage capture devices 122 and 124 included in image acquisition unit120. Image 2500 may include an image of at least one lane mark 2510, asshown in FIG. 25A. Image 2500 may also include one or more landmarks2521, such as road sign, used for navigation as described above. Someelements shown in FIG. 25A, such as elements 2511, 2530, and 2520 whichdo not appear in the captured image 2500 but are detected and/ordetermined by vehicle 200 are also shown for reference.

Using the various techniques described above with respect to FIGS. 24A-Dand 24F, a vehicle may analyze image 2500 to identify lane mark 2510.Various points 2511 may be detected corresponding to features of thelane mark in the image. Points 2511, for example, may correspond to anedge of the lane mark, a corner of the lane mark, a midpoint of the lanemark, a vertex between two intersecting lane marks, or various otherfeatures or locations. Points 2511 may be detected to correspond to alocation of points stored in a navigation model received from a server.For example, if a sparse map is received containing points thatrepresent a centerline of a mapped lane mark, points 2511 may also bedetected based on a centerline of lane mark 2510.

The vehicle may also determine a longitudinal position represented byelement 2520 and located along a target trajectory. Longitudinalposition 2520 may be determined from image 2500, for example, bydetecting landmark 2521 within image 2500 and comparing a measuredlocation to a known landmark location stored in the road model or sparsemap 800. The location of the vehicle along a target trajectory may thenbe determined based on the distance to the landmark and the landmark'sknown location. The longitudinal position 2520 may also be determinedfrom images other than those used to determine the position of a lanemark. For example, longitudinal position 2520 may be determined bydetecting landmarks in images from other cameras within imageacquisition unit 120 taken simultaneously or near simultaneously toimage 2500. In some instances, the vehicle may not be near any landmarksor other reference points for determining longitudinal position 2520. Insuch instances, the vehicle may be navigating based on dead reckoningand thus may use sensors to determine its ego motion and estimate alongitudinal position 2520 relative to the target trajectory. Thevehicle may also determine a distance 2530 representing the actualdistance between the vehicle and lane mark 2510 observed in the capturedimage(s). The camera angle, the speed of the vehicle, the width of thevehicle, or various other factors may be accounted for in determiningdistance 2530.

FIG. 25B illustrates a lateral localization correction of the vehiclebased on the mapped lane marks in a road navigation model. As describedabove, vehicle 200 may determine a distance 2530 between vehicle 200 anda lane mark 2510 using one or more images captured by vehicle 200.Vehicle 200 may also have access to a road navigation model, such assparse map 800, which may include a mapped lane mark 2550 and a targettrajectory 2555. Mapped lane mark 2550 may be modeled using thetechniques described above, for example using crowdsourced locationidentifiers captured by a plurality of vehicles. Target trajectory 2555may also be generated using the various techniques described previously.Vehicle 200 may also determine or estimate a longitudinal position 2520along target trajectory 2555 as described above with respect to FIG.25A. Vehicle 200 may then determine an expected distance 2540 based on alateral distance between target trajectory 2555 and mapped lane mark2550 corresponding to longitudinal position 2520. The laterallocalization of vehicle 200 may be corrected or adjusted by comparingthe actual distance 2530, measured using the captured image(s), with theexpected distance 2540 from the model.

FIGS. 25C and 25D provide illustrations associated with another examplefor localizing a host vehicle during navigation based on mappedlandmarks/objects/features in a sparse map. FIG. 25C conceptuallyrepresents a series of images captured from a vehicle navigating along aroad segment 2560. In this example, road segment 2560 includes astraight section of a two-lane divided highway delineated by road edges2561 and 2562 and center lane marking 2563. As shown, the host vehicleis navigating along a lane 2564, which is associated with a mappedtarget trajectory 2565. Thus, in an ideal situation (and withoutinfluencers such as the presence of target vehicles or objects in theroadway, etc.) the host vehicle should closely track the mapped targettrajectory 2565 as it navigates along lane 2564 of road segment 2560. Inreality, the host vehicle may experience drift as it navigates alongmapped target trajectory 2565. For effective and safe navigation, thisdrift should be maintained within acceptable limits (e.g., +/−10 cm oflateral displacement from target trajectory 2565 or any other suitablethreshold). To periodically account for drift and to make any neededcourse corrections to ensure that the host vehicle follows targettrajectory 2565, the disclosed navigation systems may be able tolocalize the host vehicle along the target trajectory 2565 (e.g.,determine a lateral and longitudinal position of the host vehiclerelative to the target trajectory 2565) using one or more mappedfeatures/objects included in the sparse map.

As a simple example, FIG. 25C shows a speed limit sign 2566 as it mayappear in five different, sequentially captured images as the hostvehicle navigates along road segment 2560. For example, at a first time,t₀, sign 2566 may appear in a captured image near the horizon. As thehost vehicle approaches sign 2566, in subsequentially captured images attimes t₁, t₂, t₃, and t₄, sign 2566 will appear at different 2D X-Ypixel locations of the captured images. For example, in the capturedimage space, sign 2566 will move downward and to the right along curve2567 (e.g., a curve extending through the center of the sign in each ofthe five captured image frames). Sign 2566 will also appear to increasein size as it is approached by the host vehicle (i.e., it will occupy agreat number of pixels in subsequently captured images).

These changes in the image space representations of an object, such assign 2566, may be exploited to determine a localized position of thehost vehicle along a target trajectory. For example, as described in thepresent disclosure, any detectable object or feature, such as a semanticfeature like sign 2566 or a detectable non-semantic feature, may beidentified by one or more harvesting vehicles that previously traverseda road segment (e.g., road segment 2560). A mapping server may collectthe harvested drive information from a plurality of vehicles, aggregateand correlate that information, and generate a sparse map including, forexample, a target trajectory 2565 for lane 2564 of road segment 2560.The sparse map may also store a location of sign 2566 (along with typeinformation, etc.). During navigation (e.g., prior to entering roadsegment 2560), a host vehicle may be supplied with a map tile includinga sparse map for road segment 2560. To navigate in lane 2564 of roadsegment 2560, the host vehicle may follow mapped target trajectory 2565.

The mapped representation of sign 2566 may be used by the host vehicleto localize itself relative to the target trajectory. For example, acamera on the host vehicle will capture an image 2570 of the environmentof the host vehicle, and that captured image 2570 may include an imagerepresentation of sign 2566 having a certain size and a certain X-Yimage location, as shown in FIG. 25D. This size and X-Y image locationcan be used to determine the host vehicle's position relative to targettrajectory 2565. For example, based on the sparse map including arepresentation of sign 2566, a navigation processor of the host vehiclecan determine that in response to the host vehicle traveling alongtarget trajectory 2565, a representation of sign 2566 should appear incaptured images such that a center of sign 2566 will move (in imagespace) along line 2567. If a captured image, such as image 2570, showsthe center (or other reference point) displaced from line 2567 (e.g.,the expected image space trajectory), then the host vehicle navigationsystem can determine that at the time of the captured image it was notlocated on target trajectory 2565. From the image, however, thenavigation processor can determine an appropriate navigationalcorrection to return the host vehicle to the target trajectory 2565. Forexample, if analysis shows an image location of sign 2566 that isdisplaced in the image by a distance 2572 to the left of the expectedimage space location on line 2567, then the navigation processor maycause a heading change by the host vehicle (e.g., change the steeringangle of the wheels) to move the host vehicle leftward by a distance2573. In this way, each captured image can be used as part of a feedbackloop process such that a difference between an observed image positionof sign 2566 and expected image trajectory 2567 may be minimized toensure that the host vehicle continues along target trajectory 2565 withlittle to no deviation. Of course, the more mapped objects that areavailable, the more often the described localization technique may beemployed, which can reduce or eliminate drift-induced deviations fromtarget trajectory 2565.

The process described above may be useful for detecting a lateralorientation or displacement of the host vehicle relative to a targettrajectory. Localization of the host vehicle relative to targettrajectory 2565 may also include a determination of a longitudinallocation of the target vehicle along the target trajectory. For example,captured image 2570 includes a representation of sign 2566 as having acertain image size (e.g., 2D X-Y pixel area). This size can be comparedto an expected image size of mapped sign 2566 as it travels throughimage space along line 2567 (e.g., as the size of the sign progressivelyincreases, as shown in FIG. 25C). Based on the image size of sign 2566in image 2570, and based on the expected size progression in image spacerelative to mapped target trajectory 2565, the host vehicle candetermine its longitudinal position (at the time when image 2570 wascaptured) relative to target trajectory 2565. This longitudinal positioncoupled with any lateral displacement relative to target trajectory2565, as described above, allows for full localization of the hostvehicle relative to target trajectory 2565, as the host vehiclenavigates along road 2560.

FIGS. 25C and 25D provide just one example of the disclosed localizationtechnique using a single mapped object and a single target trajectory.In other examples, there may be many more target trajectories (e.g., onetarget trajectory for each viable lane of a multi-lane highway, urbanstreet, complex junction, etc.) and there may be many more mappedavailable for localization. For example, a sparse map representative ofan urban environment may include many objects per meter available forlocalization.

FIG. 26A is a flowchart showing an exemplary process 2600A for mapping alane mark for use in autonomous vehicle navigation, consistent withdisclosed embodiments. At step 2610, process 2600A may include receivingtwo or more location identifiers associated with a detected lane mark.For example, step 2610 may be performed by server 1230 or one or moreprocessors associated with the server. The location identifiers mayinclude locations in real-world coordinates of points associated withthe detected lane mark, as described above with respect to FIG. 24E. Insome embodiments, the location identifiers may also contain other data,such as additional information about the road segment or the lane mark.Additional data may also be received during step 2610, such asaccelerometer data, speed data, landmarks data, road geometry or profiledata, vehicle positioning data, ego motion data, or various other formsof data described above. The location identifiers may be generated by avehicle, such as vehicles 1205, 1210, 1215, 1220, and 1225, based onimages captured by the vehicle. For example, the identifiers may bedetermined based on acquisition, from a camera associated with a hostvehicle, of at least one image representative of an environment of thehost vehicle, analysis of the at least one image to detect the lane markin the environment of the host vehicle, and analysis of the at least oneimage to determine a position of the detected lane mark relative to alocation associated with the host vehicle. As described above, the lanemark may include a variety of different marking types, and the locationidentifiers may correspond to a variety of points relative to the lanemark. For example, where the detected lane mark is part of a dashed linemarking a lane boundary, the points may correspond to detected cornersof the lane mark. Where the detected lane mark is part of a continuousline marking a lane boundary, the points may correspond to a detectededge of the lane mark, with various spacings as described above. In someembodiments, the points may correspond to the centerline of the detectedlane mark, as shown in FIG. 24C, or may correspond to a vertex betweentwo intersecting lane marks and at least one two other points associatedwith the intersecting lane marks, as shown in FIG. 24D.

At step 2612, process 2600A may include associating the detected lanemark with a corresponding road segment. For example, server 1230 mayanalyze the real-world coordinates, or other information received duringstep 2610, and compare the coordinates or other information to locationinformation stored in an autonomous vehicle road navigation model.Server 1230 may determine a road segment in the model that correspondsto the real-world road segment where the lane mark was detected.

At step 2614, process 2600A may include updating an autonomous vehicleroad navigation model relative to the corresponding road segment basedon the two or more location identifiers associated with the detectedlane mark. For example, the autonomous road navigation model may besparse map 800, and server 1230 may update the sparse map to include oradjust a mapped lane mark in the model. Server 1230 may update the modelbased on the various methods or processes described above with respectto FIG. 24E. In some embodiments, updating the autonomous vehicle roadnavigation model may include storing one or more indicators of positionin real world coordinates of the detected lane mark. The autonomousvehicle road navigation model may also include a at least one targettrajectory for a vehicle to follow along the corresponding road segment,as shown in FIG. 24E.

At step 2616, process 2600A may include distributing the updatedautonomous vehicle road navigation model to a plurality of autonomousvehicles. For example, server 1230 may distribute the updated autonomousvehicle road navigation model to vehicles 1205, 1210, 1215, 1220, and1225, which may use the model for navigation. The autonomous vehicleroad navigation model may be distributed via one or more networks (e.g.,over a cellular network and/or the Internet, etc.), through wirelesscommunication paths 1235, as shown in FIG. 12.

In some embodiments, the lane marks may be mapped using data receivedfrom a plurality of vehicles, such as through a crowdsourcing technique,as described above with respect to FIG. 24E. For example, process 2600Amay include receiving a first communication from a first host vehicle,including location identifiers associated with a detected lane mark, andreceiving a second communication from a second host vehicle, includingadditional location identifiers associated with the detected lane mark.For example, the second communication may be received from a subsequentvehicle travelling on the same road segment, or from the same vehicle ona subsequent trip along the same road segment. Process 2600A may furtherinclude refining a determination of at least one position associatedwith the detected lane mark based on the location identifiers receivedin the first communication and based on the additional locationidentifiers received in the second communication. This may include usingan average of the multiple location identifiers and/or filtering out“ghost” identifiers that may not reflect the real-world position of thelane mark.

FIG. 26B is a flowchart showing an exemplary process 2600B forautonomously navigating a host vehicle along a road segment using mappedlane marks. Process 2600B may be performed, for example, by processingunit 110 of autonomous vehicle 200. At step 2620, process 2600B mayinclude receiving from a server-based system an autonomous vehicle roadnavigation model. In some embodiments, the autonomous vehicle roadnavigation model may include a target trajectory for the host vehiclealong the road segment and location identifiers associated with one ormore lane marks associated with the road segment. For example, vehicle200 may receive sparse map 800 or another road navigation modeldeveloped using process 2600A. In some embodiments, the targettrajectory may be represented as a three-dimensional spline, forexample, as shown in FIG. 9B. As described above with respect to FIGS.24A-F, the location identifiers may include locations in real worldcoordinates of points associated with the lane mark (e.g., corner pointsof a dashed lane mark, edge points of a continuous lane mark, a vertexbetween two intersecting lane marks and other points associated with theintersecting lane marks, a centerline associated with the lane mark,etc.).

At step 2621, process 2600B may include receiving at least one imagerepresentative of an environment of the vehicle. The image may bereceived from an image capture device of the vehicle, such as throughimage capture devices 122 and 124 included in image acquisition unit120. The image may include an image of one or more lane marks, similarto image 2500 described above.

At step 2622, process 2600B may include determining a longitudinalposition of the host vehicle along the target trajectory. As describedabove with respect to FIG. 25A, this may be based on other informationin the captured image (e.g., landmarks, etc.) or by dead reckoning ofthe vehicle between detected landmarks.

At step 2623, process 2600B may include determining an expected lateraldistance to the lane mark based on the determined longitudinal positionof the host vehicle along the target trajectory and based on the two ormore location identifiers associated with the at least one lane mark.For example, vehicle 200 may use sparse map 800 to determine an expectedlateral distance to the lane mark. As shown in FIG. 25B, longitudinalposition 2520 along a target trajectory 2555 may be determined in step2622. Using spare map 800, vehicle 200 may determine an expecteddistance 2540 to mapped lane mark 2550 corresponding to longitudinalposition 2520.

At step 2624, process 2600B may include analyzing the at least one imageto identify the at least one lane mark. Vehicle 200, for example, mayuse various image recognition techniques or algorithms to identify thelane mark within the image, as described above. For example, lane mark2510 may be detected through image analysis of image 2500, as shown inFIG. 25A.

At step 2625, process 2600B may include determining an actual lateraldistance to the at least one lane mark based on analysis of the at leastone image. For example, the vehicle may determine a distance 2530, asshown in FIG. 25A, representing the actual distance between the vehicleand lane mark 2510. The camera angle, the speed of the vehicle, thewidth of the vehicle, the position of the camera relative to thevehicle, or various other factors may be accounted for in determiningdistance 2530.

At step 2626, process 2600B may include determining an autonomoussteering action for the host vehicle based on a difference between theexpected lateral distance to the at least one lane mark and thedetermined actual lateral distance to the at least one lane mark. Forexample, as described above with respect to FIG. 25B, vehicle 200 maycompare actual distance 2530 with an expected distance 2540. Thedifference between the actual and expected distance may indicate anerror (and its magnitude) between the vehicle's actual position and thetarget trajectory to be followed by the vehicle. Accordingly, thevehicle may determine an autonomous steering action or other autonomousaction based on the difference. For example, if actual distance 2530 isless than expected distance 2540, as shown in FIG. 25B, the vehicle maydetermine an autonomous steering action to direct the vehicle left, awayfrom lane mark 2510. Thus, the vehicle's position relative to the targettrajectory may be corrected. Process 2600B may be used, for example, toimprove navigation of the vehicle between landmarks.

Processes 2600A and 2600B provide examples only of techniques that maybe used for navigating a host vehicle using the disclosed sparse maps.In other examples, processes consistent with those described relative toFIGS. 25C and 25D may also be employed.

Crowd-Sourced 3D Points and Point Cloud Alignment

As described above, a road navigation model (such as a sparse map) maybe generated based on data collected by a plurality of vehiclestraversing a road segment. For example, each vehicle may capture imagesof landmarks, such as lane marks, road signs, traffic lights, lightposts, potholes, trees, buildings, or other features that may be presentalong a roadway. These features may be recognized by a navigation systemof the vehicle and may be transmitted to a remote server. The server maythen combine data from multiple drives along the road segment (e.g., bythe same vehicle or different vehicles) and may align the driveinformation to generate the sparse map, as described in greater detailabove.

When aligning information from vehicles moving in the same direction oftravel along the road segment, two-dimensional (2D) points may be usedto generate the sparse map. As used herein, a 2D point may include alocation of an identified feature represented in image coordinates(e.g., x and y pixel locations within the image). These 2D points may bedefined for various features identified within the images. In someembodiments, additional information, such as a feature type classifiermay also be used to define the 2D points, as described in greater detailbelow. The 2D points (and any associated type classifications) alongwith location information for the host vehicle (e.g., GPS location data)may be used to align drives in the same direction. However, these 2Dpoints may not be sufficient for aligning multiple drives in differentdirections because the same road segment may look different when viewedfrom the same direction. For example, the same road sign when viewedfrom one direction may look completely different from the oppositedirection. Therefore, it may be difficult for a system to correlatepoints representing the road sign from one direction, with pointsrepresenting the sign collected from the other direction. Accordingly,in order to fully-align drive data from opposing directions of travel,some form of “link” may be needed to correlate the collected points.

To accurately align the opposing drive information, three-dimensional(3D) points may be collected by host vehicles. For example, the 3Dpoints may be based on the image coordinates included in the 2D pointsdescribed above, as well range or depth data indicating a distance fromthe host vehicle to the road feature. Based on this additional depthinformation, data representing an object viewed from one direction maybe correlated with data representing the same object viewed from theopposite direction. In particular, a cloud of 3D points captured from afirst direction may be compared to a cloud of 3D points captured from anopposing direction. The disclosed systems may match 3D points from thetwo sets along a segment of a roadway and determine a transformationthat best aligns the two sets of points. Once detected features inimages captured in opposing drive directions have been correlated toeach other, the drive information from the different vehicles may bealigned without having to reapply the transformation. In someembodiments, the collection and transmission of 3D points may place ahigh computational demand on vehicles. Accordingly, the disclosedsystems and methods may include crowd-sourcing 3D points from multiplevehicles and aggregating the collected data into a dense 3D point cloud.

FIG. 27 illustrates an example image that may be captured by a hostvehicle for aligning drive information, consistent with the disclosedembodiments. Image 2700 may be captured by a camera of the host vehicle,such as image capture devices 122, 124, and/or 126. In the example shownin FIG. 27, the image may be captured from a front-facing camera of thehost vehicle as the vehicle travels along a road segment.

The host vehicle may be configured to recognize features and objectswithin the image. For example, image 2700 may include a road sign 2710and a pothole 2720, among other objects and/or road features. In someembodiments, the host vehicle may identify features having a recognizedor standardized classification or type. These features, which may bereferred to as “semantic” features, may include road signs, (e.g., speedlimit signs, warning signs, directional signs, etc.), potholes, trees,billboards, buildings, or any other detectable features that can berecognized and classified. For semantic features, the host vehicle maydefine the position of the feature based on one or more 2D points aswell as an object type classifier. This type classifier may allow thesystem to determine a size associated with the semantic object withoutcollecting additional points of the semantic object in order torepresent the size. For example, a 30 mph speed limit sign may berepresented by a single center point (or any other representative pointlocation) along with a type value representing the 30 mph sign. From thetype value, a server system may know that the 30 mph sign is 2 feet by3.5 feet in size. Other types of descriptors may also be collected ordetermined for detected semantic features. For example, this may includea bounding box for the semantic feature object, or the like.

In some embodiments, the host vehicle may detect other points in animage that do not have a predefined type classification, but that may berepeatedly identified or recognized based on detecting a certain point.These features, which may be referred to as “non-semantic” features, mayinclude, a tip of a pole, a corner of a building, a base of a post, orother unique points. Using an image analysis algorithm, the host vehiclemay generate a unique key based on pixel data surrounding the recognizednon-semantic feature point in the image. Other vehicles applying thealgorithm to pixel data surrounding representations of the non-semanticfeature may generate the same unique key. Accordingly, the non-semanticfeature may be recognized across multiple drives to allow alignment ofthe drive data, similar to semantic features.

In some embodiments, these semantic and non-semantic features may beidentified based on 2D points recognized in an image. For example, anavigation system for the host vehicle may be configured to identify a2D point 2712 associated with road sign 2710. This may includeperforming various edge detection or other image processing techniquesto identify road sign 2710 as described throughout the presentdisclosure. 2D point 2712 may be represented using a coordinate systemof image 2700. For example, 2D point 2712 may be represented based on xand y coordinates relative to image 2700, as shown in FIG. 27. While 2Dpoint 2712 is shown in FIG. 27 using image coordinates based on a topleft corner of image 2700, various other 2D coordinate systems may beused (e.g., having an origin in other locations of the image). 2D point2712 may further be associated with an object type classificationindicating a type for road sign 2710. For example, the object typeclassification may be a value indicating road sign 2710 is a road sign,a speed limit sign, a 30 MPH speed limit sign, or various otherclassifiers. The object type classification may be a numerical code, analphanumerical code, a semantic description, or any other suitablevalue. 2D points for non-semantic features may be detected as well. Forexample, a 2D point 2722 may be defined for a corner of pothole 2720.

As described above, the 2D image coordinates may be used to align driveinformation captured by vehicles traveling in the same direction along aroad segment. For example, because the vehicles generally follow thesame path, road sign 2710 will have the same general appearance withindifferent captured images. Accordingly, 2D point 2712 along with GPSdata or other information indicating a location of the host vehicle whenimage 2700 was captured may be used to align drive data from the samedirection of travel. For drive data collected from the oppositedirection of travel, the back of road sign 2710 may be visible but maynot appear the same as the front of road sign 2710. Thus, a 2D pointrepresenting road sign 2710 captured from the opposite direction oftravel may not easily be correlated with 2D point 2712. Accordingly, togenerate a cohesive sparse map including drive data from drives inopposite directions, 3D points may be collected by the host vehicles andtransmitted to a server. These 3D points may be generated for objectsrepresented in images captured by the host vehicles. In someembodiments, the 3D points may be based on the same objects associatedwith semantic and non-semantic features identified in the image, asdescribed above.

FIG. 28 illustrates an example 3D point 2812 that may be obtained by ahost vehicle, consistent with the disclosed embodiments. Image 2700described above may be captured by a front-facing camera 2822 of a hostvehicle 2820. For example, host vehicle 2820 may be equipped with animage capture device or camera, such as image capture devices 122, 124,and 126, as described in greater detail above, which may correspond tocamera 2822. Host vehicle 2820 may be equipped with a location sensor2824 configured to determine a location of host vehicle 2820. In someembodiments, location sensor 2824 may be a GPS receiver (which maycorrespond to position sensor 130 described above). Location sensor 2824may include other forms of sensors, including accelerometers, speedsensors, compasses, or other sensors that may help track the motion ofhost vehicle 2820. For example, a location of host vehicle 2820 may bedetermined, at least in part, based on a determined ego motion of thevehicle between detected locations. Additional details regarding egomotion estimation are provided above. Host vehicle 2820 may further beequipped with a processor, such as processing device 110, describedabove. The processor of host vehicle 2820 may be configured to analyzeimages captured by cameras mounted on host vehicle 2710 to determine 2Dand/or 3D point locations.

Host vehicle may be configured to determine a 3D location 2812associated with road sign 2710, as shown in FIG. 28. As described above,3D location 3812 may include depth information indicating a distancefrom host vehicle 2820 to road sign 2710. For example, the navigationsystem of host vehicle 2820 may determine a depth d representing adistance to 3D point 2812. Depth d may be determined or estimated invarious ways. In some embodiments, this may include structure frommotion (SfM) techniques based on a plurality of images captured usingcamera 2822. For example, camera 2822 may capture first and secondimages showing road sign 2710 at different times. This may includeanalyzing consecutively captured images, images captured at particulartime intervals, or the like. The system may determine a change inposition of road sign 2710 within the images, which may be correlatedwith a change in position of camera 2822 (e.g., determined usinglocation sensor 2824). Based on this correlation, a three-dimensionallocation of road sign 2710 may be estimated, resulting in depth d. Insome embodiments, this may include applying a matching algorithm such asa Lukas—Kanade tracker algorithm to correlate features between imagesand generate 3D points. In some embodiments, a trained machine learningmodel may be used to determine depth d. For example, a training set ofimages or groups of images along with depth data may be input into amachine learning model. The trained machine learning model may beconfigured to determine depth d based on two or more images includingroad sign 2710 captured at different positions.

In some embodiments, the change in position of camera 2822 between theimages may be based on GPS data associated with each of the images. Forexample, position sensor 2824 may determine a GPS location when a firstimage is captured and a GPS location when a second image is captured andthe distance between these locations may be used for determining 3Dpoint 2812. In some embodiments, ego motion of host vehicle 2820 may beused in place of or in addition to GPS locations. For example, a GPSlocation may be determined when the first image is captured. The systemmay then track an ego motion of host vehicle 2824 from the locationwhere the first image is captured to the location where the next imageis captured. Accordingly, the location of the second image may bedetermined based on the ego motion alone, or in combination withadditional GPS information. Due to the relatively low accuracy of GPSpositioning (e.g., 10 m accuracy, 5 m accuracy, etc.) the use of egomotion may help refine actual camera positions and improve the accuracyof depth d.

While depth d may be determined using structure from motion techniques,as described above, various other techniques may be used. In someembodiments, depth d may be determined based on data from sensors otherthan camera 2822. For example, host vehicle 2820 may include LIDARsensors, LED proximity sensors, ultrasonic sensors, laser rangefindersor other sensors that may indicate depth d. This process for determining3D points may be applied to various other objects or features within theenvironment of host vehicle 2820. For example, the same or similarprocesses as those described for FIG. 28 may be performed for pothole2720. As noted above, more points may be used for pothole 2720 sincepothole 2720 may not have a shape, size, or other characteristics thatare uniform with other potholes. Accordingly, additional points may beused to more precisely define the shape or location of pothole 2720.

In some embodiments, 3D point 2812 may be represented based on athree-dimensional real-world coordinates. For example, a coordinatesystem may be defined based on host vehicle 2820, and 3D point 2812 maybe represented as X, Y, and Z coordinates, as shown in FIG. 28. Variousother coordinate systems may be used, such as a coordinate system basedon a sparse map, a coordinate system based on a particular road segment,a global coordinate system (e.g., latitude/longitude/elevation), or anyother data defining a real-world position. The X, Y, and Z coordinatesmay be determined using the structure from motion techniques describedabove, based on LIDAR or other sensors, or various other techniques.

As a result, a group of 3D points may be determined and collected forfeatures along a road segment. These 3D points may be transmitted to aserver and used to generate a road navigation model, such as a sparsemap. FIG. 29 illustrates an example system 2900 for generating a sparsemap based on 3D point clouds, consistent with the disclosed embodiments.System 2900 may include a server 2910 configured to receive informationfrom one or more host vehicles and generate sparse maps based on thereceived data. For example, server 2910 may be configured to receivedata from vehicles 2920, 2930, and 2940, as shown in FIG. 29. Inparticular, server 2910 may collect data from vehicles 2920, 2930, and2940 travelling on a road segment at different times and may process thecollected data to generate sparse map or update an existing sparse map.Server 2910 may also transmit the sparse map or update data for thesparse map to one or more autonomous or semi-autonomous vehicles, whichmay be used for navigation. In some embodiments, server 2910 maycorrespond to sever 1230, as described above. Accordingly, any of thedescriptions or disclosures made herein in reference to server 1230 mayalso apply to server 2910, and vice versa.

Server 2910 may receive a collection of 3D points 2922, 2924, and 2926captured by host vehicle 2920. For example, host vehicle 2920 maycorrespond to host vehicle 2820, and may identify 3D points 2922, 2924,and 2926 associated with various features along a road segment, asdescribed above with respect to FIG. 28. In some embodiments, server2910 may receive additional 3D points captured by vehicles traveling inthe same direction along a road segment as host vehicle 2920. Forexample, a second host vehicle 2940 may traverse the road segment in thesame direction as host vehicle 2920 and may collect 3D pointscorresponding to the same features as one or more of 3D points 2922,2924, and 2926. Server 2910 may combine data collected from hostvehicles 2920 and 2940 (as well as other vehicles traversing the roadsegment in the same direction) and align the data, as described infurther detail above. In some embodiments, host vehicles 2920 and 2940may collect both 2D and 3D points. The 2D points may be associated withthe same features as 3D points 2922, 2924, and 2926 (e.g., as a 2Dportion of the data representing these points), or may be separatepoints. In some embodiments, server 2910 may align the data acquiredfrom host vehicles 2920 and 2940 using the 2D point data, as describedthroughout the present disclosure. It is to be understood, however, thatsimilar alignment may be performed using 3D points alone or incombination with the 2D points.

Server 2910 may further receive a group of 3D points determined by hostvehicles traveling along the road segment in an opposite direction. Forexample, server 2910 may receive 3D points 2932, 2934, and 2936 capturedby host vehicle 2930, as shown in FIG. 29. 3D points may be collected byhost vehicle 2930 as described above with respect to FIG. 28. One ormore of 3D points 2932, 2934, and 2936 may correspond to the samefeatures as 3D points 2922, 2924, and 2926. For example, 3D point 2922and 3D point 2932 may correspond to the same object along a roadsegment, such as a road sign, a light pole, a pothole, a lane mark, aroad edge, or the like. As with the 3D points captured by host vehicles2920 and 2940, server 2910 may collect 3D point data from multipledrives along the same direction of travel as host vehicle 2930.Accordingly, sever 2910 may be configured to align “crowd-sourced” setsof 3D points from different drive directions to generate the sparse map.Server may then align the set of 3D points 2922, 2924, and 2926 (orcrowd-sourced points associated with 3D points 2922, 2924, and 2926)with the set of 3D points 2932, 2934, and 2936 (or crowd-sourced pointsassociated with 3D points 2932, 2934, and 2936) to align drive datacaptured from opposing directions of travel. For example, server 2910may determine that 3D points 2922 and 2932 (and other sets of points)are associated with the same road feature and may determine atransformation to align the two sets of 3D points.

FIG. 30 illustrates an example alignment process that may be performedfor points captured from different drive directions, consistent with thedisclosed embodiments. Server 2910 may receive unaligned data 3010,which may include a plurality of points 3012 collected by vehicles in afirst direction, and a plurality of points 3014 collected by vehiclestraveling in a second direction along the same road segment. Server 2910may perform a transformation 3030 on one or both of the sets of 3Dpoints to generate aligned data 3020. The transformation may includetranslating or rotating one or both of the sets of points from opposingdirection to find a closest fit between the sets of points. For example,this may include an iterative process of translating and/or rotating aset of points to minimize the difference between the point clouds, suchas an iterative closest point (ICP) algorithm. As shown in aligned data3020, points 3012 and 3014 may be more closely aligned based ontransformation 3030. In some embodiments, not all points may corresponddirectly to points captured from the other direction of travel. Forexample, point 3022 may not have an equivalent point captured byvehicles in the opposing direction of travel. This may be due to an edgeor feature not being visible from the opposing direction, due tovariations in how points are defined, or other scenarios. Transformation3030 may include point 3022 in the aligned data, as shown in FIG. 30, ormay perform other actions, such as omitting point 3022, flagging point3022, or the like.

In some embodiments, once transformation 3030 has been performed, futuredrive data may be aligned without performing transformation 3030 as thecorrelations between points may already be defined. For example,referring to FIG. 29, server 2910 may have previously established acorrelation between points 2922 and 2932 through transformation 3030, asdescribed above. Accordingly, in later drives, when vehicle 2930 (orother vehicles traveling in the same direction as vehicle 2930) identifypoint 2932, server 2910 may associate it with point 2922. Accordingly,the drive data captured along the two directions of travel may bealigned without running an iterative closest point algorithm or otherform of transformation process. The drive data may be aligned on asegment-by-segment basis along a roadway.

In some embodiments, crowd-sourcing of the 3D points collected in thesame direction of travel may improve the alignment of drive data fromopposing directions. In particular, more 3D points may be needed toprovide a statistically significant correlation than are needed foraligning drives based on 2D points. For example, the 3D points may notbe as precise as the 2D point data due to potential errors indetermining depth d. As another example, more points may be used todefine object corners, edges, or other features using 3D points thanwith 2D points, where single points may be used to represent road signsor other commonly recognized objects. In some embodiments, a set of 3Dpoints on the order of 10× more than a number of 2D points (or greater)may be needed to accurately align drive data in the sparse map.Accordingly, a relatively large number of 3D points may be used toobtain more accurate alignments.

In some embodiments, it may not be feasible to capture the number of 3Dpoints for aligning a single drive along a particular direction oftravel. For example, the data associated with the 3D points may exceedthe processing capabilities of a processor of a single host vehicle, thestorage capabilities of the host vehicle, bandwidth limits fortransmitting data, or other limitation. Accordingly, server 2910 maycrowd source 3D point data from multiple drives in the same direction.As an illustrative example, if 200 3D points per meter of roadway are tobe collected, and each host vehicle may process and transmit up to 40 3Dpoints per meter, then server 2910 may capture 3D points from fivedrives (e.g., from five different host vehicles or from multiple drivesby the same host vehicle or vehicles) to acquire the requisite data set.If more vehicles are available for data collection, server 2910 mayacquire one point per meter from 200 vehicles (or 2 points per meterfrom 100 vehicles, etc.). In some embodiments, the number of pointsprovided by each vehicle may be configurable through settings of server2910 or individual host vehicles. For example, server 2910 may recognizea region where alignment between different drive directions is needed,and may define a number of points per meter to be collected by hostvehicles in the region. For example, this may include transmitting arequest or other information defining the requirement for 3D points. Insome embodiments, the set point for data collection may be based on anumber of vehicles traversing the road segment. For example, if the roadsegment is frequently travelled, server 2910 may reduce the number ofpoints collected per vehicle since more vehicles are available than inrural or less traveled regions. In some embodiments, the host vehiclesmay define a limit for data collection. For example, if a host vehicleis processing or transmitting other data with a higher priority, thehost vehicle may define a maximum number of 3D points per meter it iscapable of transmitting. Accordingly, the load may be distributed toother vehicles by server 2910.

FIG. 31 is a flowchart showing an example process 3100 for harvestingdata for a sparse map, consistent with the disclosed embodiments.Process 3100 may be performed by at least one processing device of ahost vehicle, such as processing unit 110, as described above. It is tobe understood that throughout the present disclosure, the term“processor” is used as a shorthand for “at least one processor.” Inother words, a processor may include one or more structures that performlogic operations whether such structures are collocated, connected, ordisbursed. In some embodiments, a non-transitory computer readablemedium may contain instructions that when executed by a processor causethe processor to perform process 3100. Further, process 3100 is notnecessarily limited to the steps shown in FIG. 31, and any steps orprocesses of the various embodiments described throughout the presentdisclosure may also be included in process 3100, including thosedescribed above with respect to FIGS. 27-30.

In step 3110, process 3100 may include receiving a plurality of imagescaptured by a camera onboard a host vehicle as the host vehicle travelsalong a road segment in a first direction. The plurality of images maybe representative of an environment of the host vehicle. For example,step 3110 may include acquiring image 2700 and similar images as hostvehicle 2820 traverses a road segment. Accordingly, the plurality ofimages may be captured by image capture devices 122, 124, and/or 126, asdescribed above.

In step 3120, process 3100 may include detecting one or more semanticfeatures represented in one or more of the plurality of images. Forexample, this may include detecting semantic features associated withroad sign 2710 and/or pothole 2720. Various other types of objects maybe detected, such as lamp posts, lane marks, road edges, trees,barriers, traffic lights, buildings, or any other objects that mayappear in images captured along a roadway. The one or more semanticfeatures may each be associated with a predetermined object typeclassification. As described above, the object type classification maybe any value identifying a type of object associated with the detectedsemantic feature.

In step 3130, process 3100 may include identifying at least one positiondescriptor associated with each of the detected one or more semanticfeatures. As used herein, a position descriptor may include anyinformation at least partially defining a location of a feature. In someembodiments, the at least one position descriptor associated with eachof the detected one or more semantic features may include atwo-dimensional image position. For example, if road sign 2710 isdetected in step 3120, step 3130 may include identifying 2D point 2712,as described above with respect to FIG. 27. The two-dimensional imageposition may be represented based on a coordinate system associated withan image. For example, the at least one two-dimensional image positionmay include an x-y position relative to at least one of the plurality ofimages.

In some embodiments, the at least one position descriptor associatedwith each of the detected one or more semantic features may include athree-dimensional point location. For example, the position descriptormay include 3D point 2812. Accordingly, the three-dimensional pointlocation may be determined based on analysis of representations of theone or more detected objects across two or more of the plurality ofimages and based on an output of at least one ego motion sensorassociated with the host vehicle, as described above. For example, thethree-dimensional point location may be determined based on an output ofposition sensor 2824. The ego motion sensor may include at least one ofa speedometer, an accelerometer, or a GPS receiver.

In step 3140, process 3100 may include identifying three-dimensionalfeature points associated one or more detected objects represented in atleast one of the plurality of images. In some embodiments, a number ofidentified position descriptors associated with detected semanticfeatures may be greater than a number of identified three-dimensionalpoints. In some embodiments, at least some of the one or more detectedobjects may be included within the one or more detected semanticfeatures. Similar to the position descriptors, the three-dimensionalpoints may correspond to portions of objects in the image. For example,the three-dimensional feature points may be associated with one or moreof edges or corners of at least one surface associated the at least someof the one or more detected objects. In some embodiments, each of thethree-dimensional feature points may include an indicator of depthrelative to the camera. For example, each of the three-dimensionalfeature points may include an x-y position relative to at least one ofthe plurality of images along with an indicator of range relative to thecamera. For example, step 3140 may include identifying 3D point 2812 asdescribed above. Accordingly, step 3140 may include determining a depthd, as shown in FIG. 28. The indicator of range may be determined invarious ways, as described above. In some embodiments, each of thethree-dimensional feature points includes an X-Y-Z location relative toa real-world origin, as shown in FIG. 28. In some embodiments, each ofthe three-dimensional feature points may be determined based on analysisof representations of the one or more detected objects across two ormore of the plurality of images and based on an output of at least oneego motion sensor associated with the host vehicle, as described above.The ego motion sensor may include at least one of a speedometer, anaccelerometer, or a GPS receiver. For example, the ego motion sensor maycorrespond to position sensor 2824.

In step 3150, process 3100 may include receiving position informationfor each of the plurality of images. The position information may beindicative of a position of the camera when each of the plurality ofimages was captured. For example, the position information may bereceived from position sensor 2824. In some embodiments, the positioninformation may include at least one indicator of position determinedbased on an output of a GPS sensor associated with the host vehicle. Insome embodiments, the position information may include at least oneindicator of position determined based on an output of at least one egomotion sensor associated with the host vehicle. In some embodiments, theposition information may be based on a combination of data from two ormore sensors. For example, the position information may include at leastone indicator of position determined based on a combination of an outputof a GPS sensor associated with the host vehicle and an output of atleast one ego motion sensor associated with the host vehicle.

In step 3160, process 3100 may include causing transmission of driveinformation for the road segment to an entity remotely-located relativeto the host vehicle. The remotely located entity may be any entitycapable of receiving and processing data. For example, step 3160 mayinclude transmitting drive information to server 2910, as describedabove. The drive information may include the identified at least onetwo-dimensional feature point, the identified three-dimensional featurepoints, and the position information. In some embodiments, the amount ofinformation transmitted to the remotely-located entity may be limited orspecified, as described above. For example, process 3100 may includeidentifying and transmitting to the remotely-located entity no more thanbetween 1 and 40 three-dimensional feature points per meter of the roadsegment. This value may vary depending on the road segment, thecapabilities of the host vehicle, the needs of the remotely-locatedentity, or other factors. In some embodiments, the number ofthree-dimensional points may be defined by the remotely-located entity.For example, process 3100 may include receiving a request from theremotely-located entity specifying a number of three-dimensional pointsper meter of the road segment to be identified and/or transmitted.

The remotely-located entity may be configured to align information frommultiple vehicles, as described above with respect to FIG. 20.Accordingly, the remotely-located entity may include one or moreprocessors configured to receive, in addition to the drive informationtransmitted by the host vehicle, drive information for the road segmentfrom each of a plurality of other vehicles. The drive information forthe road segment received from each of the plurality of other vehiclesmay include at least one position descriptor associated with eachdetected semantic feature, three-dimensional feature points for detectedobjects, and position information associated with captured images. Insome embodiments, the remotely-located entity may be configured to aligninformation from multiple drives in the same direction of travel. Forexample, the drive information for the road segment received from eachof the plurality of other vehicles may have originated from drives inwhich the plurality of other vehicles was traveling in a same directionas the host vehicle. The remotely-located entity may be configured toalign the drive information along the same direction of travel based ontwo-dimensional points, as described above. Accordingly, the one or moreprocessors may further be configured to align one or more aspects of thedrive information from the host vehicle and the drive information fromthe plurality of other vehicles based on the identified at least oneposition descriptor associated with each of the detected one or moresemantic features received from the host vehicle and based on theidentified at least one position descriptor associated with each of thedetected one or more semantic features received as part of the driveinformation received from each of the plurality of other vehicles.

In some embodiments, the remotely-located entity may be configured toalign drive information from opposing directions. For example, the driveinformation for the road segment received from each of the plurality ofother vehicles may have from drives in which at least some of theplurality of other vehicles were traveling in a same direction as thehost vehicle and in which at least some of the plurality of othervehicles were traveling in a direction opposite to the host vehicle. Theone or more processors may further be configured to align one or moreaspects of the drive information from the host vehicle and the driveinformation from the plurality of other vehicles, including the othervehicles that traveled the road segment in a direction opposite to thehost vehicle, based on the three-dimensional feature points receivedfrom the host vehicle and based on the three-dimensional feature pointsreceived from each of the plurality of other vehicles. In someembodiments, the alignment of one or more aspects of the driveinformation from the host vehicle and the drive information from theplurality of other vehicles may include correlating 3D feature pointsacquired during drives of opposite direction, as described above. Theone or more processors may further be configured to generate a sparsemap based on the aligned drive information from the host vehicle and thedrive information from the plurality of other vehicles.

FIG. 32 is a flowchart showing an example process 3200 for creating mapsused in navigating autonomous or partially autonomous vehicles,consistent with the disclosed embodiments. Process 3200 may be performedby at least one processing device of a remotely located entity, such asserver 2910, as described above. In some embodiments, a non-transitorycomputer readable medium may contain instructions that when executed bya processor cause the processor to perform process 3200. Further,process 3200 is not necessarily limited to the steps shown in FIG. 32,and any steps or processes of the various embodiments describedthroughout the present disclosure may also be included in process 3200,including those described above with respect to FIGS. 27-31.

In step 3210, process 3200 may include receiving first drive informationfor a road segment transmitted by a first plurality of vehicles thattraveled the road segment in a first direction. The first driveinformation may include a first plurality of three-dimensional featurepoints associated with objects detected by navigation systems of thefirst plurality of vehicles. For example, step 3210 may includereceiving drive information including 3D points 2922, 2924, and 2926transmitted by host vehicle 2920, as described above with respect toFIG. 29. the first drive information may be collected by a host vehicleaccording to process 3100 described above.

In step 3220, process 3200 may include receiving second driveinformation for the road segment transmitted by a second plurality ofvehicles that traveled the road segment in a second direction oppositeto the first direction. Similar to the first drive information, thesecond drive information may include a second plurality ofthree-dimensional feature points associated with objects detected bynavigation systems of the second plurality of vehicles. For example,step 3220 may include receiving 3D points 2932, 2934, and 2936 from hostvehicle 2930, as described above with respect to FIG. 29.

The three-dimensional feature points of the first drive information andthe second drive information may be associated with one or more of edgesor corners of at least one surface associated with the objects detectedby navigations systems of the first and second plurality of vehicles.For example, the three-dimensional feature points received from thefirst and second plurality of vehicles may be collected similar to 3Dpoint 2812, as described above. For example, the first plurality and thesecond plurality of three-dimensional feature points may include an x-yposition relative to at least one image along with an indicator of rangerelative to a camera. The indicator of range may be determined based ontracking across two or more plurality of images of an image positionassociated with an identified object, and an ego motion signal of a hostvehicle. For example, the first and second pluralities of points mayinclude x-y positioning of point 2712, as shown in FIG. 27, as well asdepth d, as shown in FIG. 28. In some embodiments, the first pluralityand the second plurality of three-dimensional feature points include anX-Y-Z position relative to a predetermined origin. For example, thepredetermined origin may be based on a host vehicle, a sparse map, areal-world object (e.g., a survey marker), a segment of a road, or thelike. As described above with respect to FIG. 28, the X-Y-Z position maybe determined based on tracking across two or more plurality of imagesof an image position associated with an identified object and an egomotion signal of a host vehicle.

In step 3230, process 3200 may include correlating one or more of thefirst plurality of three-dimensional feature points with one or more ofthe second plurality of three-dimensional feature points. Thiscorrelation may occur in various ways, as described above with respectto FIG. 30. In some embodiments, the correlation may be based on aniterative closest point algorithm, or similar algorithms for correlatingpoints within two point clouds.

In step 3240, process 3200 may include generating a sparse map based onthe correlation of the first plurality of three-dimensional featurepoints and the second plurality of three-dimensional feature points. Thesparse map may include drive information from the first and secondplurality of vehicles from different direction that is aligned based onthe correlation. For example, the sparse map may include at least afirst target trajectory for a lane of travel along the road segment inthe first direction and at least a second target trajectory for a laneof travel along the road segment in the second direction. Accordingly,the sparse map may be used by autonomous or semiautonomous vehicles fornavigating the lanes of travel in either direction.

As discussed above, the sparse map may require a relatively large set of3D points to accurately correlate the data from opposing drivedirections. For example, the sparse map may be based on 100three-dimensional feature points per meter of the road segment, 200three-dimensional feature points per meter of the road segment, 300three-dimensional feature points per meter of the road segment, or anyother density of points that may allow a statistically significantcorrelation between points. In some embodiments, the collection ofpoints may be crowd-sourced among several vehicles. Accordingly, thenumber of points collected per vehicle may be limited. For example, thefirst drive information and the second drive information may include nomore than between 1 and 40 three-dimensional feature points receivedfrom any one of the first plurality of vehicles or the second pluralityof vehicles per meter of the road segment. The number of points acquiredby each vehicle per meter of the road segment may be configurable and/orvariable, as described above.

In some embodiments, the sparse map may further be generated based onposition descriptors (which may include 2D and/or 3D points) collectedby the vehicles. For example, the first drive information may include afirst plurality of identified position descriptors associated withobjects detected by navigation systems of the first plurality ofvehicles, and the second drive information may include a secondplurality of identified position descriptors associated with objectsdetected by navigation systems of the second plurality of vehicles. Insome embodiments, the identified position descriptors may includetwo-dimensional points, which may be collected as described above for 2Dpoint 2712. For example, the first plurality and the second plurality ofidentified position descriptors may include an x-y position relative toat least one captured image. The generation of the sparse map mayfurther be based on a correlation of the first plurality of identifiedposition descriptors and the second plurality of identified positiondescriptors determined as part of process 3200.

As described above, the sparse map may further be generated based onposition information included in the drive information. Accordingly, thefirst drive information may include first camera position informationassociated with a first plurality of captured images, and the seconddrive information may include second camera position informationassociated with a second plurality of captured images. The positioninformation may be collected by the first and second plurality ofvehicles using one or more sensors, such as position sensor 2824. Forexample, the first camera position information and the second cameraposition information may include at least one indicator of positiondetermined based on an output of a GPS sensor, an output of an egomotion sensor, a combination of an output of a GPS sensor and an outputof at least one ego motion sensor, or any other sensors or combinationsof sensors that may indicate vehicle position. The generation of thesparse map may further be based on the first camera position informationand the second camera position information.

Two-Way Aligned Trajectories

As described above, a sparse map may be generated such that driveinformation from opposing directions of travel is aligned within thesparse map. For example, a sparse map having two-way alignedtrajectories may be generated based on crowd-sourced 3D points collectedby a plurality of vehicles, as described above. In some embodiments,vehicles may be equipped with rear-facing cameras that may be used tocorrelate objects viewed in either direction, as described in furtherdetail below (see, e.g., FIGS. 45-48 and associated descriptions). Asparse map having aligned trajectories for each direction of travel mayimprove navigation for vehicles traversing the roadway. For example, avehicle may be able to navigate based on the trajectory it is currentlytraveling along, but may also be able to make determinations in relationto other objects based on the trajectory for the opposing direction oftravel. For example, the navigation system of a host vehicle may beconfigured to assess whether an oncoming vehicle is diverging from aparticular trajectory, which may inform navigation action decisions bythe host vehicle.

FIG. 33 is an illustration of an example road segment 3300 along which ahost vehicle may navigate, consistent with the disclosed embodiments.Road segment 3300 may include one or more lanes of travel, such as lanes3302 and 3304. At least some of the lanes along road segment 3300 may beassociated with opposite directions of travel. For example, lane 3302may be associated with an opposite direction of travel than lane 3304along the road segment. As shown in FIG. 33, a host vehicle 3310 may betraveling along the road segment in lane 3302. Host vehicle 3310 may bean autonomous or semiautonomous vehicle, consistent with the disclosedembodiments. Host vehicle 3310 be the same as or similar to vehicle 200described herein. Accordingly, any of the descriptions or disclosuresmade herein in reference to vehicle 200 may also apply to host vehicle3310, and vice versa.

Host vehicle 3310 may be configured to capture images of an environmentof the host vehicle. For example, host vehicle 3310 may capture imagesusing one or more image capture devices (e.g., cameras), such as imagecapture device 122, image capture device 124, and image capture device126. Host vehicle 3310 may further be configured to detect one or moreobjects or features within road segment 3300 based on the images. Forexample, road segment 3300 may include road signs 3332 and 3334, lanemark 3336, directional arrow 3338, or various other features that may berecognized by host vehicle 3310. Host vehicle 3310 may be configured tonavigate along road segment 3300 based on detected road features andobjects, as described throughout the present disclosure. Road segment3300 may further include a target vehicle 3320 traveling in lane 3304 ina direction opposite of host vehicle 3310.

FIG. 34 illustrates an example sparse map 3400 having two-way alignedtrajectories, consistent with the disclosed embodiments. Sparse map 3400may include drive information associated with road segment 3300, asshown in FIG. 34. In some embodiments, spares map 3400 may includeadditional drive information beyond what is shown in FIG. 34. Forexample, FIG. 34 may show a portion of sparse map 3400 relevant to roadsegment 3300. The drive information may have been collected previouslyby one or more vehicles traveling along road segment 3300 andtransmitted to a server 3410. In some embodiments, server 3410 maycorrespond to sever 1230, as described above. Accordingly, any of thedescriptions or disclosures made herein in reference to server 1230 mayalso apply to server 3410, and vice versa. Server 3410 may be configuredto generate sparse map 3400 based on the received drive information. Forexample, server 3410 may collect drive information from a pluralitydrives, align information from the plurality of drives, and generatesparse map 3400 based on the aligned drive information, as describedabove (e.g., with respect to FIGS. 12-18). Server 3410 may also transmitthe sparse map (or data including updates to the sparse map) to one ormore autonomous or semi-autonomous vehicles, such as host vehicle 3310.

As shown in FIG. 34, sparse map 3400 may include one or more mappednavigational features associated with road segment 3300. In someembodiments, sparse map 3400 may include a target trajectory 3402associated with lane 3302, and a target trajectory 3404 associated withlane 3304. Target trajectories 3402 and 3404 may be generated based ondrive information from previous traversals of road segment 3300 and maybe stored in association with sparse map 3400 as three-dimensionalsplines. Sparse map 3400 may further include mapped road featuresgenerated based on drive information previously collected from vehiclestraversing road segment 3300. The mapped road features may correspond toobjects or road features identified by the vehicles that previouslytraversed road segment 3300. For example, mapped navigational feature3424 may correspond to road sign 3334 (or a corner, edge, or otherfeature of road sign 3334). Similarly, mapped navigational feature 3434may correspond to directional arrow 3338, mapped road features 3426 and3436 may correspond to road sign 3332, and mapped road features 3422 and3432 may correspond to lane marks along road segment 3300. In someembodiments, the mapped road features may include type classifiersindicating a type of the road feature. For example, mapped road feature3424 may include a type classifier indicating it is associated with aroad sign. Various other classifiers or information may also beassociated with the mapped road features.

In some embodiments, the mapped road features may have been collected byvehicles travelling in opposite directions along road segment 3400. Forexample, mapped road features 3422, 3424, and 3426 may have beencollected by one or more vehicles traveling along lane 3302 and mappedroad features 3432, 3434, and 3436 may have been collected by one ormore vehicles traveling along lane 3304. The mapped road features fromopposite directions may be aligned using various techniques disclosedthroughout the present disclosure. For example, sparse map 3400 mayinclude two-way alignment of mapped road features based on crowd-sourced3D points as described above. In some embodiments, sparse map 3400 mayinclude two-way alignment of mapped road features based on forward andrear facing cameras, as described in greater detail below.

Host vehicle 3310 may be configured to receive sparse map 3400 fromserver 3410. In some embodiments, host vehicle 3310 may be configured toreceive update data associated with sparse map 3400. For example, asserver 3410 acquires additional drive information, location data fortarget trajectories and/or mapped road features may be refined, and therefined data may be transmitted to host vehicle 3310. Host vehicle 3310may be configured to capture one or more images of road segment 3310 anddetermine navigational actions based on the captured images and sparsemap 3400. For example, host vehicle 3310 may capture an image includinga representation of road sign 3334 and may identify one or more featuresassociated with road sign 3334 in the images. Host vehicle 3310 maydetermine that the identified features are associated with mapped roadfeature 3424 and may determine a position of host vehicle 3310 relativeto target trajectory 3402. Host vehicle 3310 may determine a navigationaction based on the determined location. For example, this may include asteering maneuver to align host vehicle with target trajectory 3402.Various other navigations may be performed, such as a braking maneuver,an acceleration maneuver, a lane change maneuver, maintaining a currentcourse of action or speed, or the like.

In some embodiments, host vehicle 3310 may further determine navigationsbased on mapped navigational features in sparse map 3400 that werecaptured by vehicles traveling in an opposite direction as host vehicle3310. For example, host vehicle 3310 may identify features such asdirectional arrow 3434 or road sign 3332 that may be associated withmapped road features 3434 and 3436, respectively. As with the mappedroad features captured by vehicles traveling the same direction as hostvehicle 3310, these mapped road features captured be vehicles from theopposite direction may be used for navigation by host vehicle 3310.Accordingly, sparse map 3400 may provide a more robust collection ofmapped navigational features as compared to a sparse map where driveinformation from multiple directions of travel has not been aligned.

Further, host vehicle 3310 may assess other conditions of road segment3300 based on sparse map 3400. For example, host vehicle 3310 mayidentify a representation of an oncoming target vehicle 3320 within oneor more captured images. Based on a current position of host vehicle3310 relative to sparse map 3400 and a position of target vehicle 3320relative to host vehicle 3310 determined based on analysis of theimages, host vehicle 3310 may determine a position of target vehicle3320 relative to target trajectory 3404. Host vehicle 3310 may furtherdetermine navigation actions based on the position of target vehicle3320 relative to target trajectory 3404. If target vehicle 3404 istraveling along a path that is not consistent with target trajectory3404, this may indicate a navigation action should be taken by hostvehicle in response. For example, host vehicle 3310 may determine thattarget vehicle 3320 is veering from target trajectory 3404 in adirection towards host vehicle 3310. This may indicate that a driver oftarget vehicle 3320 is distracted or has fallen asleep, that there is anobstacle in lane 3304, that poor road conditions exist (which may alsobe present in lane 3302 ahead of host vehicle 3310), or other conditionsthat may cause target vehicle 3320 to veer off course. Accordingly, hostvehicle 3310 may perform an avoidance maneuver, such as diverging fromtarget trajectory 3402 to avoid a collision, perform an acceleration ofhost vehicle 3310, perform a braking maneuver, provide a warningindication (e.g., flashing headlights, flashing high-beam lights,sounding a horn), or the like. In some embodiments, host vehicle 3310may report target vehicle 3320. For example, if target vehicle 3320veers from target trajectory 3404 it may indicate that a drive of targetvehicle 3320 is intoxicated and host vehicle 3310 may flag targetvehicle 3320 for other vehicles traversing road segment 3300, notifylocal authorities, etc.

Various other determinations may be made by host vehicle 3310 based ontarget trajectory 3404 being aligned with target trajectory 3402 withinsparse map 3400. For example, host vehicle 3310 may recognize thattarget trajectory 3404 is associated with a drivable road surfaceaccessible to host vehicle 3310. For example, in case of an emergency(e.g., a pedestrian or animal running into lane 3302, a collisionbetween vehicles ahead of host vehicle 3310, or other unexpectedsituations), host vehicle 3310 may veer temporarily onto targettrajectory 3310, especially if no oncoming vehicles are detected. Asanother example, host vehicle 3310 may use target trajectory 3404 whenmaking a U-turn into lane 3404. In some embodiments, host vehicle 3310may warn target vehicle 3320 or other entities of various conditions ofroad segment 3300. For example, host vehicle may recognize an obstaclealong target trajectory 3404 (e.g., a pedestrian, poor road conditions,a rock or other debris, etc.) and may warn target vehicle 3320 of theconditions. This may include flashing headlights, sounding a horn,transmitting a notification signal, or other forms of warningindications. If target vehicle 3320 (or a driver of target vehicle 3320)is unable to recognize the condition, the warning from host vehicle 3310may prompt a response from target vehicle 3320.

FIG. 35 is a flowchart showing an example process 3500 for navigating anautonomous or partially autonomous host vehicle, consistent with thedisclosed embodiments. Process 3500 may be performed by at least oneprocessing device of a host vehicle, such as processing device 110. Insome embodiments, a non-transitory computer readable medium may containinstructions that when executed by a processor cause the processor toperform process 3500. Further, process 3500 is not necessarily limitedto the steps shown in FIG. 35, and any steps or processes of the variousembodiments described throughout the present disclosure may also beincluded in process 3500, including those described above with respectto FIGS. 33 and 34. In some embodiments, some or all of process 3100 maybe performed in conjunction with process 3500.

In step 3510, process 3500 may include receiving, from an entityremotely located relative to the host vehicle, a sparse map associatedwith at least one road segment. For example, host vehicle 3310 mayreceive sparse map 3400 from server 3410, as described above.Accordingly, host vehicle 3310 may be equipped with an electroniccommunications device, such as wireless transceiver 172. In someembodiments, host vehicle 3310 may be configured to store sparse map3400 in a local storage, such as map database 160. The sparse map mayinclude various information associated with the at least one roadsegment. In some embodiments, the sparse map may include a firstplurality of mapped navigational features generated based on driveinformation previously collected from a first plurality of vehicles thattraveled in a first direction along the at least one road segment. Forexample, the sparse map may include mapped road features 3422, 3424,3426, and/or target trajectory 3402. The sparse map may further includea second plurality of mapped navigational features generated based ondrive information previously collected from a second plurality ofvehicles that traveled in a second direction along the at least one roadsegment. For example, the sparse map may include mapped road features3432, 3434, 3436, and/or target trajectory 3404. The first plurality ofmapped navigational features and the second plurality of mappednavigational features may be correlated within a common coordinatesystem, as described above.

In step 3520, process 3500 may include receiving, from a cameraassociated with the host vehicle, a first plurality of images and asecond plurality of images representative of an environment of the hostvehicle as the host vehicle travels along the at least one road segmentin the first direction. For example, host vehicle 3310 may captureimages using image capture devices 122, 124, and 126 when travelingalong lane 3302 of road segment 3300.

In step 3530, process 3500 may include determining a first navigationalaction for the host vehicle based on analysis of at least one of thefirst plurality of images and based on the first plurality of mappednavigational features. In some embodiments, the first navigationalaction may align the host vehicle with a target trajectory. For example,the first plurality of mapped features may include a target trajectoryfor a lane of travel along the at least one road segment in the firstdirection and at least one mapped road feature. The mapped road featuremay include a location of the at least one mapped road feature and atype classifier associated with the at least one mapped road feature, asdescribed above. Process 3500 may further include localizing the hostvehicle relative to the target trajectory based on an identification ofa representation in the first plurality of images of the at least onemapped road feature. For example, host vehicle 3310 may identify arepresentation of road sign 3334 within the plurality of images and maycorrelate it with mapped road feature 3424. Accordingly, process 3500may include determining a position of the host vehicle relative to thesparse map based on the plurality of images.

In step 3540, process 3500 may include causing one or more actuatorsassociated with the host vehicle to implement the first navigationalaction. For example, this may include performing a steering maneuver, abraking maneuver, an acceleration maneuver, maintaining a currentheading direction or the like. In some embodiments, the firstnavigational action ay include a change in heading direction of the hostvehicle to reduce a difference between an actual trajectory of the hostvehicle and the target trajectory.

In step 3550, process 3500 may include determining a second navigationalaction for the host vehicle based on analysis of the second plurality ofimages and based on the second plurality of mapped navigationalfeatures. In some embodiments, step 3550 may include determining whethera target vehicle is traveling along a path consistent with a targettrajectory included in the sparse map. For example, process 3500 mayinclude identify a target vehicle represented in the second plurality ofimages, such as target vehicle 3320. Process 3500 may include determine,based on analysis of one or more of the second plurality of images,whether the target vehicle is traveling along a path consistent with thetarget trajectory for the lane of travel along the at least one roadsegment in the second direction. For example, process 3500 may includedetermining whether target vehicle 3320 has veered from targettrajectory 3404.

In step 3560, process 3500 may include causing the one or more actuatorsassociated with the host vehicle to implement the second navigationalaction. As with the first navigational action, the second navigationalaction may include performing a steering maneuver, a braking maneuver,an acceleration maneuver, maintaining a current heading direction or thelike. In some embodiments, the second navigational action may bedifferent than the first navigational action. In some embodiments, thesecond navigational action may be foregoing or canceling the firstnavigational action. As described above, process 3500 may includedetermining whether a target vehicle is traveling along a pathconsistent with the target trajectory for the lane of travel along theat least one road segment in the second direction. Accordingly, step3560 may include determining the second navigational action based onwhether the target vehicle is determined to be traveling along a pathconsistent with the target trajectory for the lane of travel along theat least one road segment in the second direction. In some embodiments,the second navigational action may include maintaining heading and speedof the host vehicle in response to a determination that the targetvehicle is traveling along a path consistent with the target trajectory.Conversely, the second navigational action may include at least one ofslowing the host vehicle or changing a heading of the host vehicle inresponse to a determination that the target vehicle is traveling towardthe host vehicle and not along a path consistent with the targettrajectory.

Fully Aligned Junctions

As described throughout the present disclosure, sparse maps or otherroad navigational maps may be generated based on crowd-sourced datacollected by vehicles traversing a roadway. As indicated above, withoutthe advanced techniques for collecting and correlating drive informationcaptured by the vehicles disclosed herein, it may be difficult orimpossible to align drive information from multiple directions of travelalong a road segment. For example, road features or objects may lookdifferent from opposite viewing angles and may appear in differentportions of an image. Accordingly, it may be difficult for a system tocorrelate features identified based on 2D image coordinates offront-facing cameras alone. For some use cases, sparse maps withouttwo-way aligned trajectories may be sufficient for navigating along asegment of a roadway.

However, sparse maps with target trajectories aligned in a singledirection of travel may not be sufficient for navigating within ajunction, such as an intersection. It may be beneficial for trajectorieswithin for junctions to be fully aligned because a host vehicle maytravel in any available direction offered by the junction. For example,target trajectories for left turns, right turns, u-turns, or travelingthrough the junction (including multiple possible lanes entering andexiting in each direction) may need to be fully aligned to allowvehicles to precisely navigate through the junction. This may beespecially important in junctions where vehicles may be traveling alongmultiple trajectories through the intersection at the same time. Forexample, a junction may include a stop light in which vehicles enteringthe junction from opposite directions are signaled to make left turns atthe same time. To avoid a collision with oncoming turning vehicles, thetarget trajectories associated with each of the left turns may need tobe accurately aligned.

Similar to with segments of a roadway, 3D points may be collected andcorrelated as described above. With respect to road junctions, however,this may include crowd sourcing and aligning drive information frommultiple vehicles traversing the same entrance and exit combinationthrough the junction. Then, the aligned drive information from eachentrance and exit combination may be correlated such that a sparse mapwith a fully-aligned trajectories for each entrance and exit combinationis generated.

FIG. 36A illustrates an example junction 3600 that may be traversed byone or more vehicles, consistent with the disclosed embodiments. Asshown in FIG. 36A, junction 3600 may be an intersection of two roadways,each having one lane of travel in each direction. As used herein, ajunction may include any point or region in which two or more roadwaysare joined. Accordingly, while junction 3600 is shown by way of example,it is to be understood that the disclosed systems and methods may applyin various other junction configurations. For example, junctions inaccordance with the present disclosure may include but are not limitedto four-way intersections, T-junctions, Y-intersections, traffic circles(or “roundabouts”), road forks, turn lanes, parking lots, or any otherregion with multiple traversable roadways. In some embodiments,junctions may be controlled, for example, through the use of stop signs,yield signs, traffic lights, or forms of control apparatuses. In someembodiments, junctions may be uncontrolled, where no signals or signsare included to indicate right-of-way.

In the example shown in FIG. 36A, junction 3600 may include one or moretraffic lights, such as traffic signals 3604 and 3606. Various otherobjects, such as road sign 3602 may also be included within a vicinityof junction 3600, as shown. While junction 3600 is shown as includingtwo traffic signals for purposes of simplicity, it is to be understoodthat junction 3600 may include additional traffic signals, road signs,lane markings, or other road features. Further, while junction 3600 isshown as an intersection of two roadways with single lanes of travel ineach direction, it is to be understood that junction 3600 may includeany number of roadways or lanes of travel.

Vehicles, such as host vehicle 3610 may traverse junction 3600 throughvarious combinations of entrance and exit points. FIG. 36B illustratesvarious entrance and exit combinations for junction 3600 that may betraveled by host vehicle 3610, consistent with the disclosedembodiments. In particular, as shown in FIG. 36B, junction 3600 mayinclude a plurality of entrance points 3620A, 3620B, 3620C, and 3620Dthrough which a vehicle may enter junction 3600. Further, junction 3600may include a plurality of exit points 3630A, 3630B, 3630C, and 3630D,through which a vehicle may exit junction 3600. A plurality of possibletrajectories through which a vehicle may traverse junction 3600 may bedefined by the entrance and exit points. In particular, for everypossible combination of one of entrance points 3620A, 3620B, 3620C, and3620D with one of exit points 3630A, 3630B, 3630C, and 3630D, a targettrajectory may be defined. For example, if host vehicle 3610 entersjunction 3600 through entrance point 3620A, a right-turn trajectory 3642may be available for exiting through exit point 3630B, a straighttrajectory 3644 may be available for exiting through exit point 3630C,and a left-turn trajectory 3646 may be available for exiting throughexit point 3630D. Although not shown in FIG. 36B, a u-turn targettrajectory may also be defined for entering through entrance point 3620Aand exiting through exit point 3630A. Similar combinations oftrajectories may be defined for each of entrance points 3620B, 3620C and3620D. As noted above, the disclosed systems and methods may apply toother configurations of junctions, including junctions with additionallanes in each direction. Further, junction 3600 may include turn lanesor other features that may increase the number of entrance and exitpoints. As the number of entrance and/or exit points increases, thenumber of target trajectories defined by junction 3600 may also increase(and vice versa).

As host vehicle 3610 and other host vehicles traverse junction 3600,they may collect drive information including road features associatedwith junction 3600. FIG. 37 illustrates example three-dimensional pointsthat may be collected by host vehicle 3610 while traversing junction3600, consistent with the disclosed embodiments. For example, hostvehicle 3610 may travel through junction along target trajectory 3646,as described above. While target trajectory 3646 is shown by way ofexample, similar drive information may be collected along other targettrajectories, including those shown in FIG. 36B. Host vehicle 3610 maybe configured to capture a plurality of images of the environment ofhost vehicle 3610. For example, host vehicle 3610 may capture imagesusing one or more image capture devices (e.g., cameras), such as imagecapture device 122, image capture device 124, and image capture device126. Host vehicle 3610 may be an autonomous or semiautonomous vehicle,consistent with the disclosed embodiments. Host vehicle 3610 be the sameas or similar to vehicle 200 described herein. Accordingly, any of thedescriptions or disclosures made herein in reference to vehicle 200 mayalso apply to host vehicle 3610, and vice versa.

Host vehicle 3610 may further be configured to detect one or moreobjects or features within junction 3600 based on the images. Forexample, host vehicle 3610 may identify one or more features of roadsign 3602, traffic signals 3604 and 3606, or other objects or featureswithin junction 3600. Host vehicle 3610 may be configured to determinethree dimensional points associated with the detected features. Forexample, host vehicle 3610 may identify 3D point 3702 associated withroad sign 3602, 3D points 3704, 3706, 3708, and 3710 associated withlights or other features of traffic signals 3604 and 3606, 3D point 3712associated with a lane mark of junction 3600, or various other 3Dpoints. 3D points 3702, 3704, 3706, 3708, 3710, and 3712 may berepresented in various ways. For example, the 3D points may include anx-y position relative to one or more captured images along with anindicator of range relative to the camera. The range may be determinedbased on tracking a position of an identified object over two or moreimages, along with an ego motion signal of the host vehicle (e.g., usingstructure from motion (SfM) techniques, etc.). In some embodiments, the3D points may be represented as X-Y-Z coordinates of a real-worldcoordinate system. Additional details regarding capturing 3D points froma plurality of images are provided above with respect to FIG. 28.

Host vehicle 3610 may further be configured to transmit driveinformation (including captured 3D points 3702, 3704, 3706, 3708, 3710,and 3712) to a remotely-located entity, such as server 3720. Server 3720may be configured to generate a sparse map based on drive informationassociated with junction 3600 from a plurality of vehicles. Server 3720may also transmit the sparse map (or data indicating updates to thesparse map) to one or more autonomous or semi-autonomous vehicles, whichmay be used for navigation. In some embodiments, server 3720 maycorrespond to sever 1230, as described above. Accordingly, any of thedescriptions or disclosures made herein in reference to server 1230 mayalso apply to server 3720, and vice versa.

Server 3720 may receive three dimensional points from a plurality ofvehicles traveling through junction 3600 along the same trajectory andmay align mapped road features captured by the plurality of vehicles togenerate an aligned group of three-dimensional points for each entranceand exit combination. FIG. 38 illustrates an example alignment ofthree-dimensional points collected along a common target trajectory,consistent with the disclosed embodiments. As shown in FIG. 38, server3720 may receive 3D points 3802, 3804, 3806, 3808, 3810, and 3812captured by a second host vehicle traveling through junction 3600 alongtarget trajectory 3646. Server 3720 may correlate the three-dimensionalpoints captured from multiple drives to generate an aligned 3D featurepoint group 3800 associated with target trajectory 3646. The alignmentmay be performed using an iterative closest point (ICP) algorithm, orother algorithms configured to align multiple point clouds. Server 3720may translate, rotate, scale, or otherwise transform the various groupsof three-dimensional points collected from different vehicles togenerate aligned 3D feature point group 3800. For example, 3D point 3712may be aligned with 3D point 3812, as shown in FIG. 38. While thealignment process is shown in FIG. 28 based on groups of 3D point datacollected by two vehicles, similar alignment may be performed for datafrom three or more drives.

In some embodiments, one or more 3D points within aligned 3D featurepoint group 3800 may be combined. For example, rather than includingmultiple points 3712 and 3812, aligned 3D feature point group 3800 mayinclude a single combined 3D point. Accordingly, aligned 3D featurepoint group 3800 may include a group of combined 3D feature points, eachof the combined points being associated with one mapped road feature injunction 3600. The combined points may represent an average locationamong correlated 3D points associated with the same road feature. Insome embodiments the average may be weighted based on a level ofconfidence or other value associated with a collected 3D point. Forexample, points collected during heavy fog or storms may be assigned alower confidence level than points collected during the day. Variousother factors, such as time of day, camera properties (e.g., resolution,etc.), vehicle speed, or any other factors that may affect the accuracyof determining 3D points may also be used to define a confidence level.In some embodiments, generating aligned 3D feature point group 3800 mayinclude additional processing operations. For example, server 3720 mayexclude outlier 3D points (e.g., 3D points associated with locationsdiffering from locations of other 3D points by more than a thresholdamount), or other forms of processing.

To generate an aligned sparse map for junction 3600, server 3720 maythen correlate 3D points from aligned 3D feature point group 3800 with3D points from aligned 3D feature point groups associated with otherentrance-exit combinations. FIG. 39 illustrates an example process forgenerating a sparse map 3940 based on correlated 3D points from multipleentrance-exit combinations, consistent with the disclosed embodiments.Server 3720 may generate a plurality of aligned 3D feature point groups3900 based on each entrance-exit combination for junction 3600. Forexample, aligned 3D feature point group 3902 may correspond to aligned3D feature point group 3800 and may be generated as described above withrespect to FIG. 38. Aligned 3D feature point groups 3904 and 3906 maycorrespond to trajectories 3644 and 3642, respectively. Aligned 3Dfeature point groups for each other entrance-exit combination may begenerated as well, similar to the process described above for aligned 3Dfeature point group 3800. For example, aligned 3D feature point group3908 may correspond to a trajectory based on entering junction 3600 atentrance point 3620C and exiting junction 3600 at exit point 3630B, andso on. Although not shown in FIG. 39, aligned 3D feature point groups3900 may further include aligned 3D feature point groups associated withU-turns or other trajectories through junction 3600, as noted above.

Server 3720 may correlate aligned 3D points from each of aligned 3Dfeature point groups 3900 to generate a sparse map 3940 having fullyaligned trajectories for each entrance-exit combination associated withjunction 3600 (or a selected subset of entrance-exit combinations). FIG.40 illustrates an example alignment of two sets of aligned 3D points,consistent with the disclosed embodiments. For example, FIG. 40 may showan alignment process for aligned 3D feature point group 3902, which maybe associated with target trajectory 3646, and aligned 3D feature pointgroup 3908, which may be associated with target trajectory 4010, asshown. Target trajectory 4010 may be a trajectory defined based onentering junction 3600 at entrance point 3620C and exiting junction 3600at exit point 3630B. Server 3720 may determine a correlation betweenaligned 3D feature point 4012 (from aligned 3D feature point group 3902)and aligned 3D feature point 4022 (from aligned 3D feature point group3908). Based on the correlation, server 3720 may transform one or moreof aligned 3D feature point groups 3902 and 3802 and the associatedtrajectories to align the points, as shown in FIG. 40. This may includeapplying an iterative closest point (ICP) algorithm, or other algorithmsconfigured to align multiple point clouds. The correlation and alignmentprocess may be similar to the correlation and alignment processdescribed above with respect to FIG. 30. Accordingly, any of the detailsor descriptions provided above may also apply to FIG. 40.

As a result, target trajectories 3646 and 4010 may be aligned, based onthe aligned crowd-sourced 3D points for each entrance-exit combination,as shown in FIG. 40. A similar process may be performed for each ofaligned 3D feature point groups 3900. Therefore, trajectories for eachentrance-exit combination within sparse map 3940 may be aligned witheach other. Server 3720 may then transmit sparse map 3940 to one or morevehicles, such as host vehicle 3610, which may use sparse map 3940 fornavigating junction 3600. As additional 3D points are collected byvehicles traveling through junction 3600, server 3720 may update sparsemap 3940 and may transmit an updated sparse map 3940 (or update datadefining changes over a previous sparse map) to one or more vehicles.

FIG. 41 is a flowchart showing an example process 4100 for creating mapsused in navigating autonomous or partially autonomous vehicles,consistent with the disclosed embodiments. Process 4100 may be performedby at least one processing device of a remotely located entity, such asserver 2910, as described above. In some embodiments, a non-transitorycomputer readable medium may contain instructions that when executed bya processor cause the processor to perform process 4100. Further,process 4100 is not necessarily limited to the steps shown in FIG. 41,and any steps or processes of the various embodiments describedthroughout the present disclosure may also be included in process 4100,including those described above with respect to FIGS. 27-31.

In step 4110, process 4100 may include receiving drive information fromeach of a plurality of vehicles distributed across a plurality ofvehicle groups that traverse a road junction. For example, step 4100 mayinclude receiving drive information associated with junction 3600described above. Each vehicle group may include one or more vehiclesthat traverse a different entrance-exit combination associated with theroad junction. For example, junction 3600 may be associated withtrajectories defined by various combinations of entrance points 3620A,3620B, 3620C, and 3620D with one of exit points 3630A, 3630B, 3630C, and3630D, as shown in FIG. 36B. The drive information from each of theplurality of vehicles may include three-dimensional feature pointsassociated with objects detected by analyzing images captured as aparticular vehicle traversed a particular entrance-exit combination ofthe road junction. For example, step 4110 may include receiving driveinformation from host vehicle 3610 associated with target trajectory3646, as shown in FIG. 37. Additional drive information may be collectedfrom other host vehicles as they travel along trajectory 3646 and fromhost vehicles traveling along other trajectories defined by theentrance-exit combinations.

Each of the three-dimensional feature points may be determined asdescribed above with respect to FIG. 28. For example, each of thethree-dimensional feature points includes an x-y position relative to atleast one captured image along with an indicator of range (e.g., depthd) relative to a camera that acquired the at least one captured image.The indicator of range may be determined based on tracking across two ormore images of an image position associated with an identified object,and based on an ego motion signal associated with a host vehicle. Insome embodiments, each of the three-dimensional feature points mayinclude an X-Y-Z position. Similar to the range, the X-Y-Z position maybe determined based on tracking across two or more plurality of imagesof an image position associated with an identified object, and an egomotion signal of a host vehicle. In some embodiments, collection of the3D points may be distributed across many vehicles to reduce thecomputational load on any one vehicle. For example, the driveinformation from each of the plurality of vehicles may include no morethan between 1 and 40 three-dimensional feature points per meter of theroad junction, or other suitable numbers of feature points.

In step 4120, process 4100 may include, for each of the entrance-exitcombinations, align the three-dimensional feature points received in thedrive information collected from the one or more vehicles that traversedthat entrance-exit combination to generate a plurality of alignedthree-dimensional feature point groups. Accordingly, one alignedthree-dimensional feature point group may be generated one for eachentrance-exit combination of the road junction. For example, for targettrajectory 3646, three-dimensional feature points collected by multiplevehicles traveling through junction 3600 along target trajectory 3646may be aligned as shown in FIG. 38. Similar alignment may be performedfor each entrance-exit combination to generate a plurality of alignedthree-dimensional feature point groups 3900, as described above.

In step 4130, process 4100 may include correlating one or morethree-dimensional feature points in each of the plurality of alignedthree-dimensional feature point groups with one or morethree-dimensional feature points included every other alignedthree-dimensional feature point group from among the plurality ofaligned three-dimensional feature point groups. For example, 3D featurepoints from each of aligned three-dimensional feature point groups 3900may be correlated as shown in FIGS. 39 and 40. This may include applyingan iterative closest point algorithm, or other algorithms for aligning3D point clouds.

In step 4140, process 4100 may include generating a sparse map based onthe correlation of the one or more three-dimensional feature points ineach of the plurality of aligned three-dimensional feature point groupswith one or more three-dimensional feature points included every otheraligned three-dimensional feature point group. For example, step 4140may include generating sparse map 3940, as described above. Accordingly,the sparse map may include at least one target trajectory associatedwith each of the entrance-exit combinations of the road junction. Insome embodiments, the sparse map may further include one or more mappedjunction features determined based on the drive information receivedfrom one or more of the plurality of vehicles. For example, the one ormore mapped junction features include at least one of: a traffic light,stop sign, stop line, light pole, road marking, crosswalk, building, orcurb. As discussed above, the sparse map may require a relatively largeset of 3D points to accurately correlate the trajectories. For example,the sparse map may be based on 100 three-dimensional feature points permeter of the road segment, 200 three-dimensional feature points permeter of the road segment, 300 three-dimensional feature points permeter of the road segment, or any other density of points that may allowa statistically significant correlation between points.

In some embodiments, the sparse may be further be generated based oncamera position information associated with the captured 3D points. Forexample, the drive information may include camera position informationassociated with a plurality of captured images and the generation of thesparse map may further be based on the camera position information. Thecamera position may be based on an output of one or more positionsensors, such as position sensor 2824 described above. For example, thecamera position information may include at least one indicator ofposition determined based on an output of a GPS sensor, based on anoutput of an ego motion sensor, based on a combination of an output of aGPS sensor and an output of at least one ego motion sensor, or variousother forms of sensors or combinations thereof.

Rear-Facing Camera for Two-Way Sparse Map Alignment

As described above, when aligning information from vehicles moving inthe same direction of travel along the road segment, 2D points can beused to generate the sparse map. However, these 2D points may not besufficient for aligning multiple drives in different directions becausethe same road segment may look completely different when viewed from thesame direction. For example, the same road sign when viewed from onedirection may look completely different from the opposite direction.Therefore, it may be difficult for a system to correlate pointsrepresenting the road sign from one direction, with points representingthe sign collected from the other direction. Accordingly, in order toalign drive data from opposing directions of travel, some form of “link”may be used to correlate the collected points.

One solution, described above, may be to collect 3D points from aplurality of vehicles, which can more accurately be aligned acrossmultiple driving directions. An alternative solution includes collectingimages of road feature from a rearward facing camera. For example, avehicle may capture images of a front side of an object using a forwardfacing camera and later capture images of the back side of an object.Feature points associated with the front side of the object may then becorrelated with feature points associated with the rear side of theobject, based on analysis of the images. This information may be usefulin aligning drives from different directions. For example, a second hostvehicle traveling in an opposite direction of travel from the hostvehicle may capture images of the back side of the object using afront-facing camera. Feature points identified by the second hostvehicle may be correlated with feature points collected by the firsthost vehicle based on a known relationship between the back side of theobject and the front side of the object.

As another example, a rear facing camera can be used to detect driveinformation as the host vehicle traverses a road segment, similar to afront-facing camera. That is, the rear facing camera can operate in thesame way a forward facing camera of a car traveling in a directionopposite to the host vehicle would operate. The drive informationassociated with the front-facing camera and the drive information fromthe rear-facing camera may be used to align drive information collectedby front-facing cameras of host vehicles traveling in oppositedirections along the same road segment.

FIG. 42A illustrates an example road segment 4200 along which driveinformation from multiple directions may be aligned using rear-facingcameras, consistent with the disclosed embodiments. As shown in FIG.42A, road segment 4200 may include multiple lanes, such as lanes 4222and 4224. Lanes 4222 and 4224 may be associated with differentdirections of travel. Road segment 4200 may include one or more objectsthat may be identified by host vehicles traversing road segment 4200,such as host vehicle 4210. Host vehicle 4210 may be an autonomous orsemiautonomous vehicle, consistent with the disclosed embodiments. Hostvehicle 4210 may be configured to capture a plurality of images of theenvironment of host vehicle 4210. For example, host vehicle 4210 maycapture images using a front-facing camera 4212. Front-facing camera4212 may be any form of image capture device capable of capturing imagesfacing the same direction as a direction of travel of host vehicle 4210.Host vehicle 4210 be the same as or similar to vehicle 200 describedherein. Accordingly, any of the descriptions or disclosures made hereinin reference to vehicle 200 may also apply to host vehicle 4210, andvice versa. For example, front-facing camera 4212 may correspond to oneor more of image capture device 122, image capture device 124, and imagecapture device 126 described in further detail above.

Host vehicle 4210 may further be configured to detect one or moreobjects or features within road segment 4200 based on the images. Forexample, host vehicle 4210 may identify one or more features of roadsign 4202, a crosswalk mark 4204, and/or a tree 4206. Road segment 4200may include other forms of objects and/or features not shown in FIG. 42that may be detected by host vehicle 4210. For example, this may includebut is not limited to lane marks, road boundaries, traffic lights, lightposts, potholes, trees, buildings, or other features that may be presentalong a roadway.

FIG. 42B illustrates an example image 4250 that may be captured by afront-facing camera of host vehicle 4210, consistent with the disclosedembodiments. For example, image 4250 may be captured by front-facingcamera 4212 of host vehicle 4210 when traveling along road segment 4200in the position shown in FIG. 42A. Host vehicle 4210 may identifyobjects, such as road sign 4202 and crosswalk mark 4204 in image 4250.This may include using various image processing algorithms as describedabove. Host vehicle 4210 may be configured to identify positiondescriptors associated with various features in the images. For example,host vehicle 4210 may determine a position descriptor associated withroad sign 4204. The position descriptor may include any information atleast partially defining a location of the feature. For example, theposition descriptor may include 2D point information, which may berepresented as x-y image coordinates, as described above with respect toFIG. 27.

For semantic features, having a predetermined type or othercharacteristics, host vehicle may be configured to determine an objecttype classification, as described above. For example, the object typeclassification may be a value indicating road sign 2710 is a road sign,a speed limit sign, a 30 MPH speed limit sign, or various otherclassifiers. The object type classification may be a numerical code, analphanumerical code, a semantic description, or any other suitablevalue. In some embodiments, a single position descriptor along with theobject type classification may be defined for road sign 4202. Forexample, a point 4252 may be associated with an edge of road sign 4202.Various other point locations may be used, such as a center, a corner, atop or side edge, a base point, or the like. 2D points for non-semanticfeatures may be detected as well. For example, a 2D point may be definedfor a corner of crosswalk mark 4204. As described in greater detailabove, the non-semantic feature points may be associated with a uniqueidentifier based on applying an image processing algorithm to pixelssurrounding the 2D point. Host vehicle may determine other informationand/or descriptors associated with the semantic features. For example,this may include height and width information for an identified object,information defining bounding box circumscribing the object in theimage, or other information that may identify the object.

In some embodiments, the position descriptors may include 3D points. Forexample, the position descriptors may include x-y coordinates based onan image (e.g., as defined by the 2D points described above) along withrange information indicating a distance from the camera to the object.In some embodiments, the position description may be based on X-Y-Zcoordinates based on a real-world origin point. Additional detailsregarding determining 3D points are provided above with respect to FIG.28.

Host vehicle 4210 may also be equipped with a rear-facing camera thatmay capture images of the environment of host vehicle 4210 as it travelsalong road segment 4200. FIG. 43A illustrates an example scenario forhost vehicle 4210 to capture a rear-facing image along road segment4200, consistent with the disclosed embodiments. For example, hostvehicle 4210 may be equipped with rear-facing camera 4214, as shown inFIG. 43A. Rear-facing camera 4214 may be any form of image capturedevice configured to capture images from a direction opposite thedirection of travel of host vehicle 4210 when moving forward. Rearfacing camera 4214 may correspond to one or more of image capture device122, image capture device 124, and image capture device 126. As shown inFIG. 43A, host vehicle 4210 may have traveled further along road segment4200 than in the previous position shown in FIG. 42A. For example, hostvehicle 4210 may have traveled along lane 4222 past crosswalk mark 4204and road sign 4202.

FIG. 43B illustrates an example rear-facing image 4350 that may be takenby host vehicle 4210, consistent with the disclosed embodiments. Forexample, image 4350 may be captured by rear-facing camera 4214 from theposition of host vehicle 4210 shown in FIG. 43A. As shown in FIG. 43B,image 4350 may include a back side of road sign 4204 along withcrosswalk mark 4204. Lanes 4222 and 4224 are identified in image 4350for reference. Host vehicle 4210 may be configured to identify positiondescriptors for one or more features along road segment 4200 based onimage 4350. The position descriptors may be 2D feature points (e.g.,represented based on x-y coordinates of an image) or 3D feature points(e.g., represented as a 2D point along with a range value, as real-worldX-Y-Z coordinates, etc.), as described above. In some embodiments, theroad features may include semantic features. Accordingly, the positiondescriptors may be associated with object type classificationsdetermined by host vehicle 4210. In some embodiments, the features mayinclude non-semantic features. Accordingly, the position descriptors maybe associated with unique identifiers determined based on application ofan image processing algorithm to pixels surrounding the feature point,as described above.

In the example shown in FIG. 43B, host vehicle may determine a positiondescriptor 4302 associated with road sign 4202. In some embodiments,position descriptor 4302 may be a semantic point, for example,associated with a back side of a road sign object type. In someembodiments, 2D point 4302 may be a non-semantic point that isidentified based on pixels surrounding 2D point 4302 in image 4350 suchthat it may be identified by other host vehicles in similar images.Similarly, host vehicle 4210 may be configured to identify point 4306associated with tree 4206.

In some embodiments, host vehicle 4210 may further track positioninformation associated with images 4250 and 4350. For example, hostvehicle 4210 maybe equipped with a position sensor, such as positionsensor 2824 described above. In some embodiments, the position sensormay be a GPS sensor configured to determine GPS coordinates of hostvehicle 4210. In some embodiments, the position sensor may include oneor more sensors configured to track an ego motion of host vehicle 4210.For example, the ego motion sensors may include speed sensors, steeringalignment sensors, brake sensors, accelerometers, compasses, or othersensor that may be used to track a motion of host vehicle 4210 betweentwo points. Based on known positions of front-facing camera 4212 andrear-facing camera 4414 relative to host vehicle 4210, positions of thecameras at the time images 4250 and 4350 are captured may be determined.

As shown in FIG. 43B, the reverse side of road sign 4202 has asignificantly different appearance than the front side of road sign 4202shown in FIG. 42B. For example, the post for road sign 4202 partiallycovers the sign and the printed information is not visible. Accordingly,using conventional techniques, it may be difficult to align point 4302with point 4252 if they were included in separate sets of driveinformation (e.g., captured by two different vehicles driving inopposite directions). However, the information captured by host vehicle4201 using images captured from front- and rear-facing cameras may beused to align drive information from opposing directions.

FIG. 44 illustrates example sets of drive information captured byvehicles traveling in opposite directions along road segment 4200,consistent with the disclosed embodiments. For example, a vehicle 4410may traverse road segment 4200 in a first direction along lane 4222.Vehicle 4410 may be configured to capture drive information and transmitit to a remotely-located entity, such as server 4430. Vehicle 4410 mayinclude a front-facing camera 4412 configured to capture images of theenvironment of vehicle 4410. Vehicle 4410 may identify one or morefeatures associated with objects in road segment 4200 and may determineposition descriptors associated with the identified feature points. Forexample, vehicle 4410 may identify a point 4414 associated with roadsign 4202, and a point 4416 associated with crosswalk mark 4204. Vehicle4410 may be configured to transmit drive information including points4412 and 4416, and a trajectory 4402 to server 4430.

Vehicle 4420 may traverse road segment 4200 in a direction of travelopposite to vehicle 4410 along lane 4224, as shown in FIG. 44. Whileboth vehicles 4410 and 4420 are shown on road segment 4200 at the sametime, it is to be understood that vehicles 4410 and 4420 may traverseroad segment 4200 at different times. Similar to vehicle 4410, vehicle4420 may be configured to capture drive information and transmit it to aserver 4430. Vehicle 4420 may include a front-facing camera 4422configured to capture images of the environment of vehicle 4420. Vehicle4420 may identify one or more features associated with objects in roadsegment 4200 and may determine position descriptors associated with theidentified feature points. For example, vehicle 4420 may identify apoint 4424 associated with a back side of road sign 4202, a point 4416associated with crosswalk mark 4204, and a point 4428 associated withtree 4206. Vehicle 4420 may be configured to transmit drive informationincluding points 4412 and 4416, and a trajectory 4402 to server 4430.The points shown in FIG. 44 are provided by way of example, and not allof the points shown may be needed for alignment.

Server 4430 may be configured to generate a sparse map based on driveinformation collected by vehicles 4410 and 4420. Server 4430 may alsotransmit the sparse map (or data indicating updates to the sparse map)to one or more autonomous or semi-autonomous vehicles, which may be usedfor navigation. In some embodiments, server 4430 may correspond to sever1230, as described above. Accordingly, any of the descriptions ordisclosures made herein in reference to server 1230 may also apply toserver 4430, and vice versa. Server 4430 may use drive informationcaptured by host vehicle 4210 to align the drive information captured byvehicles 4410 and 4420. For example, server 4430 may receive points4252, 4302, and/or 4306 (i.e., position descriptors), along with objecttype classifications, position information, or other informationcaptured by host vehicle 4410. This captured information may be used todetermine correlations between the drive information captured byvehicles 4410 and 4420.

The correlation may be determined in various ways. In some embodiments,the correlation may be based on detecting front sides of objects usingfront-facing camera 4212, and detecting the front sides of other objectsusing rear-facing camera. In other words, rear-facing camera 4214 canoperate just like a forward facing camera of a car traveling in adirection opposite to host vehicle 4210. Accordingly, a set of driveinformation may be generated by host vehicle 4210 for each direction oftravel along road segment 4200. Further, these two sets of driveinformation will be fully aligned because they were captured by the samevehicle along the same trajectory. When the front sides of these objectsare detected by vehicles traveling in opposite directions, the driveinformation from the two vehicles can be aligned based on the driveinformation captured by host vehicle 4210.

As an illustrative example, host vehicle 4210 may identify feature point4252 using front-facing camera 4212, as shown in FIG. 43B. Host vehicle4210 may also identify feature point 4306 for tree 4206 usingrear-facing camera 4214, as shown in FIG. 43B. Host vehicle 4210 mayidentify various other points along road segment 4200 using one or bothof front-facing camera 4212 and rear-facing camera 4214. This driveinformation may be transmitted to server 4430 by host vehicle 4210. Thefeature points captured by front-facing camera 4212 may be fully alignedwith the feature points captured using rear-facing camera 4214 as bothsets of feature points were captured along the same trajectory. Forexample, server 4430 may determine a relative position of point 4252 andpoint 4306 based on location information received from host vehicle 4210indicating the camera positions for front-facing camera 4212 andrear-facing camera 4214 when images 4250 and 4350 were captured,respectively. Based on this position information, a position of point4252 may be defined relative to point 4306. In some embodiments,additional drive information using host vehicles with front- andrear-facing cameras may be acquired and used to further refine therelative positions of points 4252 and 4306 and other points associatedwith the front sides of objects.

Server 4430 may then receive drive information from vehicles 4410 and4420, as described above. For example, server 4430 may receive driveinformation from vehicle 4410 including point 4414 and trajectory 4402.Server 4430 may further receive drive information from vehicle 4420including point 4428 and trajectory 4404. Server 4430 may correlatepoint 4414 with point 4252 because both are associated with the frontside of road sign 4202 and thus have a similar appearance in the imagedata. Similarly, server 4430 may correlate point 4428 with point 4306because both are associated with the same side of tree 4206 and thushave a similar appearance in the image. Server 4430 may align the twosets of drive information based on points 4414 and 4428 and the knownrelationship between points 4252 and 4306 (determined as described abovebased on the drive information from host vehicle 4210. Accordingly,target trajectories 4402 and 4404 may be fully aligned within a sparsemap generated by server 4430. While this alignment is described based ontwo sets of drive information for purposes of simplicity, it is to beunderstood that the same or similar processes may be performed acrossmany sets of drive information captured by a plurality of vehicles ineach direction. This example alignment process is described in furtherdetail below with respect to processes 4500 and 4600.

In some embodiments, drive information from vehicles 4410 and 4420 maybe aligned through various other techniques using drive information fromhost vehicle 4210. As another example, server 4430 may be configured tocorrelate points associated with the same object from differentdirections. For example, host vehicle 4210 may identify point 4252associated with a front side of road sign 4202 using front-facing camera4212. Host vehicle 4210 may also identify point 4302 associated with aback side of road sign 4202, as described above. Server 4430 (or hostvehicle 4210) may associated both points 4252 and 4302 with the sameobject. For example, server 4430 may receive position informationindicating the camera positions for front-facing camera 4212 andrear-facing camera 4214 when images 4250 and 4350 were captured,respectively. Based on this position information, points 4252 and 4302may be determined to be associated with front and rear sides of the sameobject, respectively.

Accordingly, additional points associated with the front side of roadsign 4202 and the rear side of road sign 4202 from different directionsmay be aligned. For example, server 4430 may receive drive informationfrom vehicle 4410 including trajectory 4402 and point 4414 associatedwith a front side of road sign 4202, as described above. Server 4430 mayfurther receive drive information from vehicle 4420 including trajectory4404 and point 4424. Server 4430 may correlate point 4414 with point4252 based on the similar appearance of road sign 4202 in image 4250 andimages captured by vehicle 4410. Similarly, server 4430 may correlatepoint 4424 with point 4302 based on the similar appearance of the backof road sign 4202 in image 4350 and images captured by vehicle 4420.Server 4430 may correlate point 4414 with point 4424 based on therelationship between points 4252 and 4302 determined by host vehicle4210. In other words, server 4430 may recognize that points 4414 and4424 represent different sides of the same object based on the driveinformation provided by host vehicle 4210. Accordingly, targettrajectories 4402 and 4404 may be fully aligned within a sparse mapgenerated by server 4430. This example alignment process is described infurther detail below with respect to processes 4700 and 4800.

FIG. 45 is a flowchart showing an example process 4500 for harvestingdata for a sparse map, consistent with the disclosed embodiments.Process 4500 may be performed by at least one processing device of ahost vehicle, such as processing unit 110, as described above. In someembodiments, a non-transitory computer readable medium may containinstructions that when executed by a processor cause the processor toperform process 4500. Further, process 4500 is not necessarily limitedto the steps shown in FIG. 45, and any steps or processes of the variousembodiments described throughout the present disclosure may also beincluded in process 4500, including those described above with respectto FIGS. 42A, 42B, 43A, 43B, and 44.

In step 4510, process 4500 may include receiving a first image capturedby a forward-facing camera onboard a host vehicle as the host vehicletravels along a road segment in a first direction. The first image maybe representative of an environment forward of the host vehicle. Forexample, step 4510 may include receiving image 4250 captured by hostvehicle 4210 traveling along road segment 4200 in lane 4222. The firstimage may be captured by a forward-facing camera, such as front-facingcamera 4212.

In step 4520, process 4500 may include receiving a second image capturedby a rearward-facing camera onboard the host vehicle as the host vehicletravels along the road segment in the first direction. The second imagemay be representative of an environment behind the host vehicle. Forexample, step 4520 may include receiving image 4350 captured by hostvehicle using rear-facing camera 4214, as shown in FIGS. 43A and 43B.

In step 4530, process 4500 may include detecting a first semanticfeature represented in the first image. For example, step 4530 mayinclude detecting road sign 4202 in image 4250. In some embodiments, thefirst semantic feature may be associated with a predetermined objecttype classification. For example, the object type classification may bea value indicating road sign 4202 as a road sign, a speed limit sign, a30 MPH speed limit sign, or various other classifiers. The object typeclassification may be a numerical code, an alphanumerical code, asemantic description, or any other suitable value. While road sign 4204is used by way of example, it is understood that the semantic featuremay include various other features, such as a speed limit sign, a yieldsign, a pole, a painted directional arrow, a traffic light, a billboard,or a building.

In step 4540, process 4500 may include identifying at least one positiondescriptor associated with the first semantic feature represented in thefirst image captured by the forward-facing camera. For example, step4540 may include identifying a position descriptor associated with point4252, as described above. In some embodiments, the at least one positiondescriptor associated with the first semantic feature may include an x-yimage position relative to the first image. For example, the positiondescriptor may be a 2D point as described above with respect to FIG. 27.In some embodiments, the at least one position descriptor associatedwith the first semantic feature may include an X-Y-Z position relativeto a predetermined origin. For example, the position descriptor may be a3D point as described above with respect to FIG. 28. In someembodiments, the X-Y-Z position may be determined based on tracking achange in image position of the first semantic feature between the firstimage and at least one additional image and based on an output of atleast one ego motion sensor associated with the host vehicle. Forexample, the at least one ego motion sensor may include at least one ofa speedometer, an accelerometer, or a GPS receiver.

In step 4550, process 4500 may include detecting a second semanticfeature represented in the second image. For example, step 4550 mayinclude detecting tree 4206 in image 4350. Similar to the first semanticfeature, the second semantic feature may be associated with apredetermined object type classification. For example, the object typeclassification may be a value indicating tree 4206 is a foliage object,a tree, a pine-tree, or various other classifiers. While tree 4206 isused by way of example, it is understood that the semantic feature mayinclude various other features, such as a speed limit sign, a yieldsign, a pole, a painted directional arrow, a traffic light, a billboard,or a building.

In step 4560, process 4500 may include identifying at least one positiondescriptor associated with the second semantic feature represented inthe second image captured by the rearward-facing camera. For example,step 4560 may include identifying a position descriptor associated withpoint 4306, as described above. In some embodiments, the at least oneposition descriptor associated with the second semantic feature mayinclude an x-y image position relative to the first image. For example,the position descriptor may be a 2D point as described above withrespect to FIG. 27. In some embodiments, the at least one positiondescriptor associated with the second semantic feature may include anX-Y-Z position relative to a predetermined origin. For example, theposition descriptor may be a 3D point as described above with respect toFIG. 28. In some embodiments, the X-Y-Z position may be determined basedon tracking a change in image position of the first semantic featurebetween the second image and at least one additional image and based onan output of at least one ego motion sensor associated with the hostvehicle. For example, the at least one ego motion sensor may include atleast one of a speedometer, an accelerometer, or a GPS receiver.

In step 4570, process 4500 may include receiving position informationindicative of a position of the forward-facing camera when the firstimage was captured and indicative of a position of the rearward-facingcamera when the second image was captured. These camera positions may bebased on an output of one or more position sensors, such as positionsensor 2824 described above. For example, the position information mayinclude at least one indicator of position determined based on an outputof a GPS sensor associated with the host vehicle, based on an output ofat least one ego motion sensor associated with the host vehicle, orbased on a combination of an output of a GPS sensor associated with thehost vehicle and an output of at least one ego motion sensor associatedwith the host vehicle.

In step 4580, process 4500 may include causing transmission of driveinformation for the road segment to an entity remotely-located relativeto the host vehicle. For example, step 4580 may include causingtransmission of the drive information to server 4430 using wirelesstransceiver 172, as described above. The drive information may includevarious information collected by the host vehicle. In some embodiments,the drive information may include the at least one position descriptorassociated with the first semantic feature, the at least one positiondescriptor associated with the second semantic feature, and the positioninformation. In some embodiments, the drive information may furtherinclude one or more descriptors associated with each of the first andsecond semantic features. For example, the one or more descriptors mayinclude a height or a width, a bounding box, a type classification, orvarious other information, as described above.

In some embodiments, the remotely-located entity may be configured toreceive drive information from a plurality of other vehicles andcorrelate the received drive information based on the drive informationtransmitted by the host vehicle. For example, the remotely-locatedentity includes one or more processors configured to receive, inaddition to the drive information transmitted by the host vehicle, driveinformation from a first plurality of other vehicles that travel alongthe road segment in the first direction along with drive informationfrom a second plurality of other vehicles that travel along the roadsegment in a second direction opposite to the first dimension. Forexample, this may include drive information received by vehicles 4410and 4420, as described above. The one or more processors may further beconfigured to correlate one or more aspects of the drive informationreceived from the first plurality of other vehicles with one or moreaspects of the drive information received from the second plurality ofother vehicles based, at least in part, upon the first and secondsemantic features, as described above. In some embodiments, thecorrelation is further based on the position information.

FIG. 46 is a flowchart showing an example process 4600 for creating mapsused in navigating autonomous or partially autonomous vehicles,consistent with the disclosed embodiments. Process 4600 may be performedby at least one processing device of a remotely located entity, such asserver 4430, as described above. In some embodiments, a non-transitorycomputer readable medium may contain instructions that when executed bya processor cause the processor to perform process 4600. Further,process 4600 is not necessarily limited to the steps shown in FIG. 46,and any steps or processes of the various embodiments describedthroughout the present disclosure may also be included in process 4600,including those described above with respect to FIGS. 42A, 42B, 43A,43B, and 44.

In step 4610, process 4600 may include receiving first drive informationfor a road segment transmitted by a first plurality of vehicles thattraveled the road segment in a first direction. For example, step 4610may include receiving drive information for road segment 4200transmitted by vehicle 4410, as described above. Accordingly, the firstplurality of vehicles may be traveling along road segment 4200 in lane4222.

In step 4620, process 4600 may include receiving second driveinformation for the road segment transmitted by a second plurality ofvehicles that traveled the road segment in a second direction oppositeto the first direction. For example, step 4620 may include receivingdrive information for road segment 4200 transmitted by vehicle 4420, asdescribed above. Accordingly, the first plurality of vehicles may betraveling along road segment 4200 in lane 4224.

In step 4630, process 4600 may include receiving, from at least onevehicle equipped with a forward-facing camera and a rearward-facingcamera, third drive information for the road segment. For example, step4630 may include receiving drive information from host vehicle 4210captured while traveling along road segment 4200. In some embodiments,the third drive information may be collected and transmitted accordingto at least some aspects of process 4400 described above. In someembodiments, the third drive information may include a positiondescriptor associated with a first semantic feature detected based onanalysis of a forward-facing image captured by the forward-facingcamera; and a position descriptor associated with a second semanticfeature detected based on analysis of a rearward-facing image capturedby the rearward-facing camera. In some embodiments, the positiondescriptor associated with the first semantic feature detected based onanalysis of the forward-facing image, the second semantic featuredetected based on analysis of the rearward-facing image, or both, mayinclude an x-y position relative to the forward-facing image. Forexample, one or both of the position descriptors may be defined as a 2Dpoint as described above with respect to FIG. 27. In some embodiments,the position descriptor associated with the first semantic featuredetected based on analysis of the forward-facing image, the secondsemantic feature detected based on analysis of the rearward-facingimage, or both, may include an X-Y-Z position relative to apredetermined origin. For example, one or both of the positiondescriptors may be defined as a 3D point as described above with respectto FIG. 28.

In step 4640, process 4600 may include correlating one or more aspectsof the first drive information and the second drive information based,at least in part, upon the position descriptor associated with the firstsemantic feature detected based on analysis of the forward-facing imageand upon the position descriptor associated with the second semanticfeature detected based on analysis of the rearward-facing image capturedby the rearward-facing camera. For example, the position descriptorassociated with the first semantic feature may include point 4252, andthe position descriptor associated with the second semantic feature mayinclude point 4306. Step 4640 may include correlating positions of point4416 (which may be included in the first drive information) and point4428 (which may be included in the second drive information) based on arelationship between points 4252 and 4306 determined based on the thirddrive information, as described above.

In some embodiments, the third drive information may further include anindicator of a position of the forward-facing camera when theforward-facing image was captured, and an indicator of a position of therearward-facing camera when the rearward-facing image was captured. Asdescribed above, the correlation of the one or more aspects of the firstdrive information and the second drive information may further be basedon the indicator of a position of the forward-facing camera when theforward-facing image was captured and based on the indicator of aposition of the rearward-facing camera when the rearward-facing imagewas captured. For example, this may include an indicator of the positionof front-facing camera 4212 when image 4250 was taken and an indicatorof the position of rear-facing camera 4214 when image 4350 was taken. Insome embodiments, these camera positions may be based on an output ofone or more position sensors associated with the at least one vehicleequipped with a forward-facing camera and a rearward-facing camera, suchas position sensor 2824 described above. For example, the indicator of aposition of the forward-facing camera position and the indicator of aposition of the rearward-facing camera may be determined based on anoutput of a GPS sensor, an output of at least one ego motion sensor, ora combination of an output of a GPS sensor and an output of at least oneego motion sensor, both the GPS sensor and the at least one ego motionsensor being associated with the at least one vehicle equipped with aforward-facing camera and a rearward-facing camera.

In step 4650, process 4600 may include generating the sparse map based,at least in part, on the correlation of the first drive information andthe second drive information. The sparse map may include at least afirst target trajectory for a lane of travel along the road segment inthe first direction and at least a second target trajectory for a laneof travel along the road segment in the second direction. For example,the sparse map may include target trajectories 4402 and 4404, asdescribed above.

FIG. 47 is a flowchart showing an example process 4700 for harvestingdata for a sparse map, consistent with the disclosed embodiments.Process 4700 may be performed by at least one processing device of ahost vehicle, such as processing unit 110, as described above. In someembodiments, a non-transitory computer readable medium may containinstructions that when executed by a processor cause the processor toperform process 4700. Further, process 4700 is not necessarily limitedto the steps shown in FIG. 47, and any steps or processes of the variousembodiments described throughout the present disclosure may also beincluded in process 4700, including those described above with respectto FIGS. 42A, 42B, 43A, 43B, and 44.

In step 4710, process 4700 may include receiving a first image capturedby a forward-facing camera onboard a host vehicle as the host vehicletravels along a road segment in a first direction. The first image maybe representative of an environment forward of the host vehicle. Forexample, step 4710 may include receiving image 4250 captured by hostvehicle 4210 traveling along road segment 4200 in lane 4222. The firstimage may be captured by a forward-facing camera, such as front-facingcamera 4212.

In step 4720, process 4700 may include receiving a second image capturedby a rearward-facing camera onboard the host vehicle as the host vehicletravels along the road segment in the first direction. The second imagemay be representative of an environment behind the host vehicle. Forexample, step 4720 may include receiving image 4350 captured by hostvehicle using rear-facing camera 4214, as shown in FIGS. 43A and 43B.

In step 4730, process 4700 may include detecting at least one objectrepresented in the first image. For example, step 4730 may includedetecting road sign 4202 in image 4250.

In step 4740, process 4700 may include identify at least one front sidetwo-dimensional feature point. The front side two-dimensional featurepoint may be associated with the at least one object represented in thefirst image. For example, step 4740 may include detecting point 4252, asdescribed above. In some embodiments, the at least one front sidetwo-dimensional feature point may include an x-y position relative tothe first image. For example, the front side two-dimensional featurepoint may be a 2D point, as described above with respect to FIG. 27.

In step 4750, process 4700 may include detecting a representation of theat least one object in the second image. For example, step 4750 mayinclude detecting the back side of road sign 4202 in image 4350. In someembodiments, the representation of the at least one object in the secondimage may be detected based on a predetermined relationship between theforward-facing camera and the rearward-facing camera. For example, thepositions of front-facing camera 4212 and rear-facing camera 4214 may bedefined relative to host vehicle 4210. In some embodiments, therepresentation of the at least one object in the second image mayfurther be detected based on an output of at least one ego motion sensorassociated with the host vehicle. For example, step 4750 may includetracking a motion of host vehicle 4210 from a location where image 4250was captured and detecting the back side of road sign 4202 image 4350once host vehicle 4210 reaches a position where the back side of roadsign 4202 is expected to be visible. Host vehicle 4210 may detect theback side of road sign 4202 based on an expected position of road sign4202 within image 4350. For example, based on the relationship betweenfront-facing camera 4212 and rear-facing camera 4214 and the ego motionof host vehicle 4210, an expected position of the back side of road sign4202 may be determined, which may be used to identify the back side ofroad sign 4202 in image 4350.

In step 4760, process 4700 may include identifying at least one rearside two-dimensional feature point. The at least one rear sidetwo-dimensional feature point may be associated with the at least oneobject represented in the second image. For example, step 4760 mayinclude detecting point 4302, as described above. In some embodiments,the at least one rear side two-dimensional feature point may include anx-y position relative to the first image. For example, the front sidetwo-dimensional feature point may be a 2D point, as described above withrespect to FIG. 27.

In step 4770, process 4700 may include receiving position informationindicative of a position of the forward-facing camera when the firstimage was captured and indicative of a position of the rearward-facingcamera when the second image was captured. These camera positions may bebased on an output of one or more position sensors, such as positionsensor 2824 described above. For example, the position information mayinclude at least one indicator of position determined based on an outputof a GPS sensor associated with the host vehicle, based on an output ofat least one ego motion sensor associated with the host vehicle, orbased on a combination of an output of a GPS sensor associated with thehost vehicle and an output of at least one ego motion sensor associatedwith the host vehicle.

In step 4780, process 4700 may include causing transmission of driveinformation for the road segment to an entity remotely-located relativeto the host vehicle. For example, step 4770 may include causingtransmission of the drive information to server 4430 using wirelesstransceiver 172, as described above. The drive information may includevarious information collected by the host vehicle. In some embodiments,the drive information may include the at least one front sidetwo-dimensional feature point, the at least one rear sidetwo-dimensional feature point, and the position information. In someembodiments, the drive information may further include an indicator thatthe at least one front side two-dimensional feature point and the atleast one rear side two-dimensional feature point are associated withthe same object. For example, this may include a reference or otherinformation linking the front side and rear side two-dimensional featurepoints.

In some embodiments, the remotely-located entity may be configured toreceive drive information from a plurality of other vehicles andcorrelate the received drive information based on the drive informationtransmitted by the host vehicle. For example, the remotely-locatedentity includes one or more processors configured to receive, inaddition to the drive information transmitted by the host vehicle, driveinformation from a first plurality of other vehicles that travel alongthe road segment in the first direction along with drive informationfrom a second plurality of other vehicles that travel along the roadsegment in a second direction opposite to the first dimension. Forexample, this may include drive information received by vehicles 4410and 4420, as described above. The one or more processors may further beconfigured to correlate one or more aspects of the drive informationreceived from the first plurality of other vehicles with one or moreaspects of the drive information received from the second plurality ofother vehicles based, at least in part, upon the first and secondsemantic features, as described above. In some embodiments, thecorrelation is further based on the position information. In someembodiments, the correlation may further be based on an indicatorreceived as part of the drive information received from the host vehiclethat the at least one front side two-dimensional feature point and theat least one rear side two-dimensional feature point are associated withthe same object.

FIG. 48 is a flowchart showing an example process 4800 for creating mapsused in navigating autonomous or partially autonomous vehicles,consistent with the disclosed embodiments. Process 4800 may be performedby at least one processing device of a remotely located entity, such asserver 4430, as described above. In some embodiments, a non-transitorycomputer readable medium may contain instructions that when executed bya processor cause the processor to perform process 4800. Further,process 4800 is not necessarily limited to the steps shown in FIG. 48,and any steps or processes of the various embodiments describedthroughout the present disclosure may also be included in process 4800,including those described above with respect to FIGS. 42A, 42B, 43A,43B, and 44.

In step 4810, process 4800 may include receiving first drive informationfor a road segment transmitted by a first plurality of vehicles thattraveled the road segment in a first direction. For example, step 4810may include receiving drive information for road segment 4200transmitted by vehicle 4410, as described above. Accordingly, the firstplurality of vehicles may be traveling along road segment 4200 in lane4222.

In step 4820, process 4800 may include receiving second driveinformation for the road segment transmitted by a second plurality ofvehicles that traveled the road segment in a second direction oppositeto the first direction. For example, step 4810 may include receivingdrive information for road segment 4200 transmitted by vehicle 4420, asdescribed above. Accordingly, the first plurality of vehicles may betraveling along road segment 4200 in lane 4224.

In step 4830, process 4800 may include receiving, from at least onevehicle equipped with a forward-facing camera and a rearward-facingcamera, third drive information for the road segment. For example, step4810 may include receiving drive information from host vehicle 4210captured while traveling along road segment 4200. In some embodiments,the third drive information may be collected and transmitted accordingto at least some aspects of process 4400 described above. In someembodiments, the third drive information may include at least one frontside two-dimensional feature point generated based on analysis of arepresentation of an object in a forward-facing image captured by theforward-facing camera. For example, the third drive information mayinclude point 4252 generated based on analysis of a front of road sign4202 in image 4250. In some embodiments, the at least one front sidetwo-dimensional feature point may include an x-y position relative tothe first image. For example, the front side two-dimensional featurepoint may be a 2D point, as described above with respect to FIG. 27.

The third drive information may further include at least one rear sidetwo-dimensional feature point generated based on analysis of arepresentation of the object in a rearward-facing image captured by therearward-facing camera. For example, the third drive information mayinclude point 4302 generated based on analysis of a back side of roadsign 4202 in image 4350. In some embodiments, the at least one rear sidetwo-dimensional feature point may include an x-y position relative tothe second image. For example, the rear side two-dimensional featurepoint may be a 2D point, as described above with respect to FIG. 27.

In step 4840, process 4800 may include correlating one or more aspectsof the first drive information and the second drive information based,at least in part, upon the at least one front side two-dimensionalfeature point and upon the at least one rear side two-dimensionalfeature point included in the third drive information. For example, thefirst drive information may include a point 4414, and the second driveinformation may include point 4424. Step 4840 may include correlatingpositions of point 4414 and point 4424 based on a relationship betweenpoints 4252 and 4302 determined based on the third drive information, asdescribed above.

In some embodiments, the third drive information may further include anindicator of a position of the forward-facing camera when theforward-facing image was captured, and an indicator of a position of therearward-facing camera when the rearward-facing image was captured. Asdescribed above, the correlation of the one or more aspects of the firstdrive information and the second drive information may further be basedon the indicator of a position of the forward-facing camera when theforward-facing image was captured and based on the indicator of aposition of the rearward-facing camera when the rearward-facing imagewas captured. For example, this may include an indicator of the positionof front-facing camera 4212 when image 4250 was taken and an indicatorof the position of rear-facing camera 4214 when image 4350 was taken. Insome embodiments, these camera positions may be based on an output ofone or more position sensors associated with the at least one vehicleequipped with a forward-facing camera and a rearward-facing camera, suchas position sensor 2824 described above. For example, the indicator of aposition of the forward-facing camera position and the indicator of aposition of the rearward-facing camera may be determined based on anoutput of a GPS sensor, an output of at least one ego motion sensor, ora combination of an output of a GPS sensor and an output of at least oneego motion sensor, both the GPS sensor and the at least one ego motionsensor being associated with the at least one vehicle equipped with aforward-facing camera and a rearward-facing camera. In some embodiments,the third drive information further includes an indicator that the atleast one front side two-dimensional feature point and the at least onerear side two-dimensional feature point are associated with the sameobject. The correlation of the one or more aspects of the first driveinformation and the second drive information may further be based on theindicator that the at least one front side two-dimensional feature pointand the at least one rear side two-dimensional feature point areassociated with the same object.

In step 4850, process 4800 may include generating the sparse map based,at least in part, on the correlation of the first drive information andthe second drive information. The sparse map may include at least afirst target trajectory for a lane of travel along the road segment inthe first direction and at least a second target trajectory for a laneof travel along the road segment in the second direction. For example,the sparse map may include target trajectories 4402 and 4404, asdescribed above.

Bandwidth Management for Map Generation and Refinement

As described elsewhere in this disclosure, harnessing and interpretingvast volumes of data (e.g., captured image data, map data, GPS data,sensor data, etc.) collected by vehicles poses a multitude of designchallenges. For example, the data collected by a vehicle may need to beuploaded to a server. The sheer quantity of data to be uploaded mayeasily cripple or hinder the transmission bandwidth of the vehicle.Additionally, analyzing new data by a server to update the relevantportion of a map based on the new data may also be challenging.Moreover, different density levels may be used for mapping differenttypes of features. For example, a density level of 330 kB per km may beneeded for mapping non-semantic features, compared with about a densitylevel of 20 kB per km for semantic features. Given the computationresources required for collecting non-semantic feature information andcertain hard limit bandwidth caps (e.g., 100 MB per year) that may beimposed to each of the vehicles, there may be insufficient resourcesavailable to the vehicles to collect non-semantic feature informationall the time.

The systems and methods may enable controlling not only whether drivedata is collected, but also when and how drive data is collected. Forexample, the disclosed systems and methods may enable a server todetermine whether a host vehicle is entering a zone including a regionof interest. Upon confirmation that the vehicle has entered the zone,the server can cause the vehicle to begin collecting higher densitynon-semantic features information. If the vehicle is determined to havedriven through the point of interest, then the server can cause thevehicle to upload the collected non-semantic features information. Ifthe vehicle is determined not to have driven through the region ofinterest, the server may cause the host vehicle to discard the collectednon-semantic features information. The disclosed systems and methods mayalso enable the server to update a map based on the non-semanticfeatures information collected by the vehicle.

FIG. 49 illustrates an exemplary system for automatically generating anavigational map relative to one or more road segments, consistent withthe disclosed embodiments. As illustrated in FIG. 49, system 4900 mayinclude a server 4901, one or more vehicles 4902, and one or morevehicle devices 4903 associated with a vehicle, a database 4904, and anetwork 4905. For example, vehicle 4902 and/or vehicle device 4903 maybe configured to collect, at a first density level, first navigationalinformation associated with the environment traversed by vehicle 4902when vehicle 4902 travels outside of a predetermined distance from ageographical region of interest. Vehicle 4902 and/or vehicle device 4903may also be configured to collect, at a second density level that may begreater than the first density level, second navigational informationassociated with the environment traversed by vehicle 4902 when vehicle4902 travels at or within the predetermined distance from thegeographical region of interest.

Server 4901 may be configured to receive the first and/or secondnavigational information associated with the environment traversed byvehicle 4902. Database 4904 may be configured to store information forthe components of system 4900 (e.g., server 4901, vehicle 4902, and/orvehicle device 4903). Network 4905 may be configured to facilitatecommunications among the components of system 4900.

Server 4901 may be configured to cause collection of first navigationalinformation associated with an environment traversed by vehicle 4902.The first navigational information may be collected at a first densitylevel. Server 4901 may also be configured to determine the location ofvehicle 4902 based on output associated with a GPS sensor associatedwith vehicle 4902. Server 4901 may further be configured to determinewhether vehicle 4902 is at or within a predetermined distance from thegeographical region of interest (or a boundary thereof). Server 4901 mayalso be configured to cause collection of second navigationalinformation associated with the environment traversed by vehicle 4902,based on the determination that the location of vehicle 4902 is at orwithin the predetermined distance from the geographical region ofinterest. The second navigational information may be collected at asecond density level that may greater than the first density level.Server 4901 may further be configured to cause vehicle 4902 to upload atleast one of the collected first navigational information or thecollected second navigational information (or a portion thereof) fromvehicle 4902. Server 4901 may also be configured to update anavigational map based on the uploaded at least one of the collectedfirst navigational information or the collected second navigationalinformation. In some embodiments, server 4901 may be a cloud server thatperforms the functions disclosed herein. The term “cloud server” refersto a computer platform that provides services via a network, such as theInternet. In this example configuration, server 4901 may use virtualmachines that may not correspond to individual hardware. For example,computational and/or storage capabilities may be implemented byallocating appropriate portions of desirable computation/storage powerfrom a scalable repository, such as a data center or a distributedcomputing environment. In one example, server 4901 may implement themethods described herein using customized hard-wired logic, one or moreApplication Specific Integrated Circuits (ASICs) or Field ProgrammableGate Arrays (FPGAs), firmware, and/or program logic which, incombination with the computer system, cause server 4901 to be aspecial-purpose machine.

Vehicle 4902 and/or vehicle device 4903 may be configured to collectfirst navigational information when it travels beyond the predetermineddistance from the geographical region of interest (or a boundarythereof). Vehicle 4902 and/or vehicle device 4903 may also be configuredto collect second navigational information when it travels at or withinthe predetermined distance from the geographical region of interest (ora boundary thereof). Vehicle 4902 and/or vehicle device 4903 may furtherbe configured to upload at least one the collected first navigationalinformation and the collected second navigational information (or aportion thereof). In some embodiments, vehicle 4902 may receive mapinformation (e.g., an updated map) from server 4901. Map information mayinclude data relating to the position in a reference coordinate systemof various items, including, for example, roads, water features,geographic features, businesses, points of interest, restaurants, gasstations, a sparse data model including polynomial representations ofcertain road features (e.g., lane markings), target trajectories for thehost vehicle, or the like, or a combination thereof. In someembodiments, vehicle 4902 and/or vehicle device 4903 may also beconfigured to plan a routing path and/or navigate vehicle 4902 accordingto the map information. For example, vehicle 4902 and/or vehicle device4903 may be configured to determine a route to a destination based onthe map information. Alternatively or additionally, vehicle 4902 and/orvehicle device 4903 may be configured to perform at least onenavigational action (e.g., making a turn, stopping at a location, etc.)based on the received map information. In some embodiments, vehicle 4902may include a device having a similar configuration and/or performingsimilar functions as system 100 described above. Alternatively oradditionally, vehicle device 4903 may have a similar configurationand/or performing similar functions as system 100 described above.

Database 4904 may include a map database configured to store mapinformation for the components of system 4900 (e.g., server 4901,vehicle 4902, and/or vehicle device 4903). In some embodiments, server4901, vehicle 4902, and/or vehicle device 4903 may be configured toaccess database 4904, and obtain data stored from and/or upload data todatabase 4904 via network 4905. For example, server 4901 may transmitdata relating to map information to database 4904 for storage. Vehicle4902 and/or vehicle device 4903 may download map information and/or datafrom database 4904. In some embodiments, database 4904 may include datarelating to the position, in a reference coordinate system, of variousitems, including roads, water features, geographic features, businesses,points of interest, restaurants, gas stations, or the like, or acombination thereof. In some embodiments, database 4904 may include adatabase similar to map database 160 described elsewhere in thisdisclosure.

Network 4905 may be any type of network (including infrastructure) thatprovides communications, exchanges information, and/or facilitates theexchange of information between the components of system 4900. Forexample, network 4905 may include or be part of the Internet, a LocalArea Network, wireless network (e.g., a Wi-Fi/302.11 network), or othersuitable connections. In other embodiments, one or more components ofsystem 4900 may communicate directly through dedicated communicationlinks, such as, for example, a telephone network, an extranet, anintranet, the Internet, satellite communications, off-linecommunications, wireless communications, transponder communications, alocal area network (LAN), a wide area network (WAN), a virtual privatenetwork (VPN), and so forth.

FIGS. 50A, 50B, and 50C illustrate an exemplary process for collectingnavigational information, consistent with disclosed embodiments. Asillustrated in FIG. 50A, vehicle 4902 may travel in a region 5000.Server 4901 may be configured to determine the location of vehicle 4902based on, for example, GPS data received from a GPS device associatedwith vehicle 4902. Server 4901 may also be configured to determine thatvehicle 4902 is beyond a predetermined distance d from geographicalregion of interest 5001. Server 4901 may further be configured to causevehicle 4902 to collect, at a first density level, first navigationalinformation associated with the environment traversed by vehicle 4902.Vehicle 4902 may move in region 5000 as illustrated in FIG. 50B. Server4901 may be configured to determine that vehicle 4902 is within thepredetermined distance d from geographical region of interest 5001 basedon the location of vehicle 4902. Server 4901 may also be configured tocause vehicle 4902 to collect, at a second density level that may begreater than the first density level, second navigational informationassociated with the environment traversed by vehicle 4902. Vehicle 4902may move in region 5000 as illustrated in FIG. 50C.

A greater density level may indicate a greater amount of collected dataper a unit length of a road segment. For example, the secondnavigational information collected at the second density level mayinclude a denser set of similar objects (e.g., more landmarks) than thefirst navigational information collected at the first density level. Insome embodiments, the density level may correspond to a number ofthree-dimensional points that are captured per meter. For example, asdescribed in further detail above, a host vehicle may capturethree-dimensional points associated with features in the environment ofthe vehicle, which may be used to align drive information from differentdirections. The disclosed systems and methods may be used to definedifferent densities of three-dimensional points to be collected. Variousother aspects associated with three-dimensional point collection may bedefined, such as controlling whether or not the three-dimensional pointsare collected, defining a type of features to be associated withthree-dimensional points (e.g., semantic versus non-semantic, etc.), orvarious other aspects.

Alternatively or additionally, the second navigational informationcollected at the second density level may include more categories ofdata collected than the first navigational information collected at thefirst density level. For example, the second navigational informationcollected at the second density level may include more non-sematicfeatures (e.g., descriptors), which may need more data compared to thatfor sematic features, than the first navigational information has. Insome embodiments, the second navigational information may include one ormore non-sematic features, while the first navigational information maynot include any non-sematic features. Examples of semantic features andnon-semantic features were discussed earlier in this disclosure.Alternatively or additionally, the second navigational informationcollected at the second density level may include sensor data from moreand/or different sensors than the first navigational informationcollected at the first density level. For example, the firstnavigational information may include GPS data received from a GPSsensor. The second navigational information may include GPS data andLIDAR data received from a LIDAR system. As another example, the firstnavigational information may include GPS data and LIDAR data, and thesecond navigational information may include GPS data and image (and/orVIDAR) data received from a VIDAR system. As still another example, thefirst navigational information may include one or more images capturedby a camera installed on the vehicle, and the second navigationalinformation may include one or more images captured by the camera andLIDAR data received from a LIDAR system. As still another example, thefirst navigational information may include the data received from anaccelerometer sensor and data received from a LIDAR system, and the datareceived from the accelerometer sensor and image (and/or VIDAR) datareceived from a VIDAR system.

In some embodiments, a landmark may include a combination of objects (orlandmarks) as long as the combination of objects is distinguishable (itcan be distinguished from other nearby sets of objects). For example,landmarks of the same type, including a set of landmarks that mayconstitute a combination landmark (a landmark that may be constituted bya combination of semantic objects) may distanced apart from one anotherby a certain distance (e.g., the distance that corresponds (or is equal)to the minimal resolution of the GPS device associated with a vehicle.

Server 4901 may be configured to determine that vehicle 4902 is withingeographical region of interest 5001. Vehicle 4902 may be configured tocontinue to collect second navigational information at the seconddensity level when it travels within geographical region of interest5001. Server 4901 may also be configured to cause vehicle 4902 to uploadat least one of the first navigational information and the secondnavigational information to server 4901.

In some embodiments, one or more attributes of the drive may be assessedto determine whether the first and/or second navigational informationshould be uploaded. For example, a system may identify a need for dataassociated with a particular road segment to be collected. For example,it may be difficult to determine whether a vehicle will travel along theroad segment until after the vehicle passes through a region surroundingthe road segment. Accordingly, an area of interest surrounding the roadsegment may be defined such that a configuration of how data iscollected is changed. For example, as vehicle 4902 enters a region 5001surrounding the road segment, it may be triggered to collect secondnavigation information at the second density level, as described above.In some embodiments, the large amount of data collected in the secondnavigation information may be preferred only if the vehicle traverses aparticular road segment of interest. Accordingly, the second navigationinformation may be discarded if vehicle 4902 does not traverse the roadsegment of interest. Vehicle 4902 may transmit location information,such as GPS data to server 4901. In response, server 4901 may analyzethe GPS data to determine whether vehicle 4902 entered the road segment(or another region of interest). If so, server 4901 may transmitinstructions to vehicle 4902 to upload the full (or partial) set ofnavigation information.

In some embodiments, one or more rules may be implemented to define howmuch data should be collected and/or saved. For example, this mayinclude geographic rules as described above. As another example, a rulemay define a frequency at which data is uploaded to a server. In someembodiments, a frequency rule may control a rate at which vehiclesupload data to a server, which may allow data to be spread across theday rather than when a vehicle traverses a particular road segment. Insome embodiments, the rule may define a rate at which vehicles collectand upload data associated with a particular road segment. For example,a frequency rule may define a period of 34 minutes. If a serveridentifies a drive in the road segment at 11:00 AM, drives by othervehicles through the same road segment will not collect and/or uploaddata until 11:34 AM. Accordingly, the frequency at which data isreceived may be limited. In some embodiments, a server may accept datafor an entire drive by a vehicle, even if data from other road segmentswithin the drive is not required by an associated frequency ruleassociated with those segments. For example, a host vehicle may uploaddata from a first segment according to a first frequency rule and mayinclude data from a second road segments, even if a second frequencyrule for the second road segment does not indicate data should beuploaded.

FIG. 51 is a flowchart showing an exemplary process for automaticallygenerating a navigational map relative to one or more road segments,consistent with the disclosed embodiments. One or more steps of process5100 may be performed by a vehicle (e.g., vehicle 4902), a deviceassociated with the host vehicle (e.g., vehicle device 4903), and/or aserver (e.g., server 4901). While the descriptions of process 5100provided below use server 4901 as an example, one skilled in the artwould appreciate that one or more steps of process 5100 may be performedby a vehicle (e.g., vehicle 4902) and a vehicle device configured tocollect navigational information and transmit the navigationalinformation to a server. In addition, in various steps described herein,a vehicle may upload certain information and/or data to a server, andone skilled in the art would understand that, alternatively oradditionally, the vehicle may upload the information and/or data to adatabase (e.g., database 4904) via a network. Moreover, one skilled inthe art would understand that although the descriptions of process 5100provided below use vehicle 4902 as an example, process 5100 is notlimited to one vehicle. For example, server 4901 may be configured todetermine the locations of a plurality of vehicles and cause theplurality of vehicles to collect first and/or second navigationalinformation. Server 4901 may also be configured to cause the pluralityof vehicles to upload the collected first and/or second navigationalinformation, and update a navigational map based on the received firstand/or second navigational information.

At step 5101, first navigational information associated with anenvironment traversed by a host vehicle may be collected. For example,server 4901 may be configured to cause collection of first navigationalinformation, by vehicle 4902 at a first density level, associated withan environment traversed by vehicle 4902. By way of example, asillustrated in FIG. 50A, vehicle 4902 may travel in a region 5000.Server 4901 may be configured to cause vehicle 4902 to collectnavigational information associated with vehicle 4902's environment bytransmitting a request to vehicle 4902 to collect navigationalinformation at the first level.

In some embodiments, vehicle 4902 may collect navigational informationdetermined based on signals received from one or more sensors, such as aGPS device, a speed sensor, an accelerometer, a suspension sensor, acamera, and a LIDAR device, or the like, or a combination thereof.Exemplary navigational information may include information and/or datarelating to the locations of the vehicle, speeds of the vehicle, drivingdirections of the vehicle, landmarks associated with one or more roadsegments, roads, one or more road features, one or more infrastructureobjects, water features, geographic features, points of interest (e.g.,office buildings, restaurants, gas stations, etc.), one or more imagesassociated with one or more road segments, LIDAR data associated withone or more road segments, a sparse data model including polynomialrepresentations of certain road features (e.g., lane markings), targettrajectories for the host vehicle, or the like, or a combinationthereof. For example, the first navigational information may includelocation information associated with one or more landmarks associatedwith the one or more road segments. Alternatively or additionally, thefirst navigational information may include location informationassociated with one or more road features associated with the one ormore road segments.

In some embodiments, the first navigational information may beassociated with a first density level. A density level may indicatecertain categories of navigational information to collect (e.g., one ormore types of navigational information collected), a collection rate ofnavigational information (e.g., how many data points per unit time, suchas per second, per minute, per hour, etc.), an accuracy level ofnavigational information (e.g., a higher or lower data resolution), orthe like, or a combination thereof. In some embodiments, a density levelmay be associated with a data density (e.g., less than 10 MB perkilometer of roads, less than 5 MB per kilometer of roads, 2 MB perkilometer of roads, less than 1 MB per kilometer of roads, less than 500kB per kilometer of roads, less than 100 kB per kilometer of roads, lessthan 10 kB per kilometer of roads, or less than 2 kB per kilometer ofroads).

In some embodiments, vehicle 4902 may collect first navigationalinformation without input or a request from server 4901. For example,vehicle 4902 may operate in a “default” collection mode in whichnavigational information is collected at a predetermined density level(e.g., the first density level). Alternatively, vehicle 4902 may collectnavigational information at a predetermined density level based on asetting associated with a condition of vehicle 4902 (e.g., a speed,heading direction, etc.). As yet another alternative, vehicle 4902 maycollect navigational information based on a density level transmittedfrom server 4901 to vehicle 4902.

At step 5102, a location of the host vehicle may be determined based onoutput associated with one or more sensors of the host vehicle. Forexample, vehicle 4902 may be configured to receive output associatedwith a GPS sensor and transmit the output to server 4901. Server 4901may be configured to determine the location of vehicle 4902 based onreceived output associated with the GPS sensor. Alternatively oradditionally, vehicle 4902 may be configured to determine its locationbased on the received output associated with the GPS sensor and transmitthe location of vehicle 4902 to server 4901. Exemplary sensors mayinclude a speed sensor, an accelerometer, a camera, a LIDAR device, orthe like, or a combination thereof. Alternatively or additionally,vehicle 4902 may use map information to determine a location.

In some embodiments, the location of the vehicle 4902 may be determinedat predetermined time intervals. For example, the location of vehicle4902 may be determined once every second or every minute. Alternativelyor additionally, the location of vehicle 4902 may be determined based onthe speed of the vehicle 4902. For example, the location of vehicle 4902may be determined more frequently if it is determined (by vehicle 4902and/or server 4901) that vehicle 4902 travels at a higher speed.Alternatively or additionally, the location of vehicle 4902 may bedetermined based on the region in which vehicle 4902 is. For example,the location of vehicle 4902 may be determined more frequently if it isdetermined (by vehicle 4902 and/or server 4901) that vehicle 4902 is inan urban area (or less frequently if vehicle 4902 is in a rural area).

At step 5103, whether the location of the host vehicle is at or within apredetermined distance from a geographical region of interest may bedetermined. For example, as illustrated in FIGS. 50A and 50B, server4901 may be configured to determine a geographical region of interest5001 and a predetermined distance d from geographical region of interest5001 (or a boundary thereof). Server 4901 may also be configured todetermine whether vehicle 4902 is at or within the predetermineddistance d from geographical region of interest 5001 (or a boundarythereof). As illustrated in FIG. 50A, server 4901 may be configured todetermine that vehicle 4902 is not at or within the predetermineddistance d from geographical region of interest 5001 (or a boundarythereof). Alternatively, as illustrated in FIG. 50B, server 4901 may beconfigured to determine that vehicle 4902 is at or within thepredetermined distance d from geographical region of interest 5001 (or aboundary thereof). Alternatively or additionally, vehicle 4902 may beconfigured to determine whether vehicle 4902 is at or within thepredetermined distance d from geographical region of interest 5001 (or aboundary thereof). For example, server 4901 may transmit a geographicalregion of interest 5001 and a predetermined distance d from geographicalregion of interest 5001 (or a boundary thereof) to vehicle 4902. Vehicle4902 may be configured to determine whether vehicle 4902 is at or withinthe predetermined distance d from geographical region of interest 5001(or a boundary thereof) based on the location of the vehicle 4902determined as described elsewhere in this disclosure. Vehicle 4902 maybe configured to transmit the result of the determination of being at orwithin (or outside of) the predetermined distance d from geographicalregion of interest 5001 (or a boundary thereof) to server 4901.

In some embodiments, server 4901 may be configured to determine ageographical region of interest based on existing information of thegeographical region of interest. For example, server 4901 may beconfigured to determine that the existing map data of a region may beinadequate (e.g., does not include certain details regarding a roadfeature associated with a road segment in the region). By way ofexample, server 4901 may be configured to determine a particular roadsegment (e.g., 1 km in length) that may need accurate alignment. Server4901 may also be configured to determine a geographical region ofinterest (e.g., a square or circle that covers the road segment). Asanother example, server 4901 may determine to update map information ofa region and may designate the region as a geographical region ofinterest.

A geographical region of interest may be a geographic region in a map ora coordinate system. A geographical region of interest may include oneor more of road segments, streets, intersections, highways, highwayjunctions, landmarks, infrastructure objects, geographic features,points of interest (such as businesses, restaurants, gas stations,etc.), or the like, or a combination thereof. Geographical regions ofinterest may vary in shape and/or size. A geographical region ofinterest may include a triangular shape, a quadrilateral shape, aparallelogram shape, a rectangular shape, a square (or substantiallysquare) shape, a trapezoid shape, a diamond shape, a hexagon shape, anoctagon shape, a circular (or substantially circular) shape, an ovalshape, an egg shape, or the like, or a combination thereof. One skilledin the art would understand that the shape of a geographical region ofinterest is not limited to the exemplary shapes described in thisdisclosure. Other shapes are also possible. For example, a geographicalregion of interest may include an irregular shape (e.g., determinedbased on one or more boundaries of jurisdictions, such as countries,states, counties, cities, and/or roads) and/or a portion of any of theshapes described herein. The size of a geographical region of interestmay be in a range of 1 square meter to 100 square kilometers. In someembodiments, the size of a geographical region of interest may berestricted into a subrange of 1 square meter to 0.1 square kilometers,0.1 to 0.25 square kilometers, 0.25 square kilometers to 1 squarekilometer, 1 square kilometers to 10 square kilometers, and 10 to 25square kilometers, 25 to square kilometers, and 50 to 100 squarekilometers.

In some embodiments, the predetermined distance may include a directdistance, a route distance (e.g., a distance for the host vehicle totravel to the geographical region of interest), or the like, or acombination thereof. The predetermined distance may be in a range of 1meter to 100 kilometers. In some embodiments, the predetermined distancemay be restricted into subranges of 1 to 5 meters, 5 to 10 meters, 10 to50 meters, 50 to 100 meters, 100 to 500 meters, 500 to 1000 meters, 1 to5 kilometers, 5 to 10 kilometers, 10 to 50 kilometers, and 50 to 100kilometers. Alternatively or additionally, the predetermined distancemay include a distance indicator based on the separation of the vehiclefrom the geographical region of interest (or a boundary thereof) interms of the number of street blocks, the number of exits on a highway,or the like, or a combination thereof. For example, a predetermineddistance may include a distance indicator indicating three streetblocks.

In some embodiments, server 4901 may be configured to determine anapproximation of a geographical region based on the geographical regionof interest. For example, server 4901 may extend the geographical regionof interest by adding the predetermined distance to the boundaries ofthe geographical region of interest to obtain a rough geographicalregion. Server 4901 (and/or vehicle 4902) may also be configured todetermine whether vehicle 4902 is at or within the rough geographicalregion based on the location of vehicle 4902.

In some embodiments, server 4901 may transmit information relating tothe geographical region of interest and/or predetermined distance tovehicle 4902. For example, server 4901 may transmit the boundaries ofthe geographical region of interest and/or the predetermined distance tovehicle 4902.

At step 5104, in response to the determination that the location of thehost vehicle is at or within the predetermined distance from thegeographical region of interest, second navigational informationassociated with the environment traversed by the host vehicle may becollected. For example, server 4901 may be configured to causecollection of second navigational information associated with theenvironment traversed by vehicle 4902, based on the determination thatthe location of vehicle 4902 is at or within the predetermined distancefrom the geographical region of interest.

For example, as illustrated in FIG. 50B, server 4901 may be configuredto determine that vehicle 4902 is within geographical region of interest5001. Server 4901 may also be configured to transmit an instruction tovehicle 4902 to collect second navigational information, and vehicle4902 may collect second navigational information in response to thereceived instruction.

In some embodiments, the second navigational information may includemore categories of information than the first navigational information.For example, the first navigational information may include landmarksand road features associated with one or more road segments (i.e., twocategories of information). The second navigational information mayinclude not only landmarks and road features associated with one or moreroad segments, but also one or more infrastructure objects, waterfeatures, geographic features, and/or points of interest associated withone or more road segments.

In some embodiments, the second navigational information may beassociated with a second density level that is higher than the firstdensity level. For example, the second density level may be at least twotimes greater than the first density level. By way of example, the firstnavigational information may include cloud point data measured at afirst density level (e.g., 300 points every 15 meters) by a LIDAR deviceassociated with vehicle 4902. The second navigational information mayinclude cloud point data measured by the LIDAR device at a seconddensity level (e.g., 800 points every 15 meters), which is more than twotimes greater than the first density level. As another example, thesecond density level may be at least five times greater than the firstdensity level. As another example, the second density level may be atleast ten times greater than the first density level.

Alternatively or additionally, the second navigational information maybe collected at a collection rate higher than a collection rate at whichthe first navigational information is collected. By way of example, thefirst navigational information may include images of the environment ofvehicle 4902 captured by a camera onboard vehicle 4902 at a first imagecollection rate. The second navigational information may include imagesof the environment of vehicle 4902 captured by the camera onboardvehicle 4902 at a second image collection rate that is higher than thefirst image collection rate. An image collection rate may include acollection rate based on time and/or distance that vehicle 4902 travels.For example, a first image collection rate may be 10 frames per second(FPS), and a second image collection rate may be 30 or 60 FPS. Asanother example, a first image collection rate may be 1 frame per meterthat vehicle 4902 travels, and a second image collection rate may be 5or 10 frames per meter that vehicle 4902 travels. Alternatively oradditionally, the second navigational information is associated with animage resolution from a camera onboard vehicle 4902 that is higher thanan image resolution associated with the first navigational information.For example, the first navigational information may include one or moreimages captured by a camera onboard vehicle 4902 at a first resolutionof 1392×1024, and the second navigational information may include one ormore images captured by the camera onboard vehicle 4902 at a secondresolution of 1600×1200 (or higher).

As described elsewhere in this disclosure, exemplary second navigationalinformation may include information and/or data relating to thelocations of the vehicle, speeds of the vehicle, driving directions ofthe vehicle, landmarks associated with one or more road segments, roads,one or more road features, one or more infrastructure objects, waterfeatures, geographic features, points of interest (e.g., officebuildings, restaurants, gas stations, etc.), one or more imagesassociated with one or more road segments, LIDAR data associated withone or more road segments, a sparse data model including polynomialrepresentations of certain road features (e.g., lane markings), targettrajectories for the host vehicle, or the like, or a combinationthereof. For example, the second navigational information may includelocation information associated with one or more landmarks associatedwith the one or more road segments. Alternatively or additionally, thesecond navigational information may include location informationassociated with one or more road features associated with the one ormore road segments.

In some embodiments, the first navigational information and/or secondnavigational information may also include video detection and ranging(VIDAR) data. For example, vehicle 4902 may include a set of camerasconfigured to generate a dense point cloud of the environment of thevehicle at a particular point in time using the VIDAR technology. By wayof example, vehicle 4902 (and/or server 4901) may generate the cloud bylearning monocular features and correlation features to infer the depthat each pixel in a subset of the cameras. In some embodiments, the firstnavigational information and/or second navigational information may bothLIDAR and VIDAR data. In some embodiments, the LIDAR and VIDAR data maybe collected around the same time.

In some embodiments, the first navigational information may includeLIDAR data, and the second navigational information may include VIDARdata, which may have a greater density level than that of LIDAR data.

In some embodiments, vehicle 4902 may be configured to continue tocollect the second navigational information if it is determined thatvehicle 4902 is at or within the predetermined distance from thegeographical region of interest (or a boundary thereof). For example,server 4901 (and/or vehicle 4902) may be configured to determine thelocations of vehicle 4902 and determine whether vehicle 4902 is at orwithin the predetermined distance from the geographical region ofinterest (or a boundary thereof) as described elsewhere in thisdisclosure. Server 4901 may transmit an instruction to vehicle 4902 tocontinue to collect the second navigational information unless asubsequent instruction to switch to collecting the first navigationalinformation is received by vehicle 4902. In some embodiments, server4901 (and/or vehicle 4902) may be configured to determine whethervehicle 4902 travels beyond the predetermined distance from geographicalregion of interest 5001 after it has been determined that vehicle 4902is at or within the predetermined distance from geographical region ofinterest 5001. Server 4901 (and/or vehicle 4902) may also be configuredto cause vehicle 4902 to switch back to collecting the firstnavigational information.

At step 5105, at least one of the collected first navigationalinformation or the collected second navigational information from thehost vehicle may be uploaded. For example, server 4901 may be configuredto cause vehicle 4902 to upload at least one of the collected firstnavigational information or the collected second navigationalinformation (or a portion thereof) from vehicle 4902. Vehicle 4902 mayupload at least one of the collected first navigational information orthe collected second navigational information via a transceiverassociated with vehicle 4902. In some embodiments, vehicle 4902 maytransmit the first navigational information and/or second navigationalinformation (or a portion of the first navigational information and/or aportion of the second navigational information) to server 4901continuously. Alternatively, vehicle 4902 may transmit the firstnavigational information and/or second navigational information (or aportion of the first navigational information and/or a portion of thesecond navigational information) to server 4901 intermittently. Forexample, vehicle 4902 may transmit the first navigational informationand/or second navigational information (or a portion of the firstnavigational information and/or a portion of the second navigationalinformation) to server 4901 a number of times over a period of time. Byway of example, vehicle 4902 may transmit the first navigationalinformation and/or second navigational information (or a portion of thefirst navigational information and/or a portion of the secondnavigational information) to server 4901 once per minute. Alternatively,the vehicle may transmit the first navigational information and/orsecond navigational information (or a portion of the first navigationalinformation and/or a portion of the second navigational information)when vehicle 4902 has access to a more reliable and/or faster network(e.g., having a stronger wireless signal, via a WIFI connection, etc.).In some embodiments, vehicle 4902 may the first navigational informationand/or second navigational information (or a portion of the firstnavigational information and/or a portion of the second navigationalinformation) upon a request received from server 4901. For example,server 4901 may transmit a request to vehicle 4902 requestingtransmission of a subset of the first navigational information or thesecond navigational information collected by vehicle 4902. Vehicle 4902may transmit the requested navigational information (e.g., the subset ofthe first navigational information or the second navigationalinformation) based on the received request. In some embodiments, therequest may include at least one time stamp.

In some embodiments, vehicle 4902 may be configured to upload both firstand second navigational information to server 4901, and server 4901 maybe configured to receive and store both the first navigationalinformation and the second navigational information.

In some embodiments, server 4901 may be configured to cause vehicle 4902to upload the collected second navigational information (or a portionthereof) based on a determination of whether vehicle 4902 traveledwithin the geographical region of interest. For example, as illustratedin FIG. 51C, server 4901 may be configured to determine that vehicle4902 traveled within geographical region of interest 5001. Server 4901may be configured to cause vehicle 4902 to upload the collected secondnavigational information (or a portion thereof) to server 4901.Alternatively or additionally, server 4901 may be configured to causevehicle 4902 to upload a portion of the collected second navigationalinformation that is associated with geographical region of interest 5001to server 4901. In some embodiments, server 4901 may also be configuredto cause vehicle 4902 to discard a portion of the collected secondnavigational information that is not associated with geographical regionof interest 5001.

In some embodiments, server 4901 may be configured to determine thatvehicle 4902 did not travel within the geographical region of interest.Server 4901 may further be configured to cause vehicle 4902 not toupload all of or at least a portion of the second navigationalinformation if vehicle 4902 did not travel within the geographicalregion of interest. Alternatively or additionally, server 4901 may beconfigured to cause vehicle 4902 to discard (and/or overwrite) all of orat least a portion of the second navigational information (or mark allor at least a portion of the second navigational information as beingeligible for being overwritten) based on the determination that vehicle4902 did not travel within the geographical region of interest. In someembodiments, server 4901 may be configured to determine whether vehicle4902 traveled within geographical region of interest 5001 afterdetermining that vehicle 4902 vehicle approached to within thepredetermined distance to geographical region of interest 5001 and thenmoved away to at least the predetermined distance away from geographicalregion of interest 5001.

In some embodiments, vehicle 4902 may upload the first navigationalinformation and/or the second navigational information without input oran instruction from server 4901. For example, vehicle 4902 may transmitthe first and/or second navigational information (and/or sensor data) toserver 4901 when such information is collected. Alternatively, vehicle4902 may transmit the first and/or navigational information (and/orsensor data) to server 4901 upon a trigger event. For example, vehicle4902 may determine that a predetermined period of time or apredetermined distance has passed since the last semantic object isdetected. Vehicle 4902 may also upload the first and/or secondnavigational information (and/or sensor data) collected to server 4901.

At step 5106, a navigational map be updated. For example, server 4901may be configured to update the navigational map based on the uploadedat least one of the collected first navigational information or thecollected second navigational information. For example, server 4901 maybe configured to update the navigational map associated with thegeographical region of interest (and/or other region(s) based on thereceived first and/or second navigational information collected byvehicle 4902. Server 4901 may also be configured to store the updatednavigational map into a storage and/or a database (e.g., database 4904).In some embodiments, server 4901 may transmit the updated navigationalmap to one or more vehicles. For example, server 4901 may transmit theupdated navigational map to vehicle 4902 via, for example, network 4905.Alternatively or additionally, server 4901 may store the updatednavigational map into database 4904, and one or more vehicles (e.g.,vehicle 4902) may obtain the updated navigational map from database4904.

In some embodiments, server 4901 may update a navigational map based on(first and/or second) navigational information received from two or morevehicles. For example, server 4901 may aggregate first and/or secondnavigational information received from two or more vehicles that areassociated with the same road segment. In some embodiments, thenavigational information collected when the vehicle drives in adirection along a road segment may be different from the navigationalinformation collected when the vehicle drives in the opposite directioneven along the same road segment. Server 4901 may aggregate first and/orsecond navigational information received from two or more vehicles thatare associated with the same road segment in the same driving direction(and/or a direction opposite to the same driving direction). Forexample, server 4901 may align the navigational information receivedfrom two or more vehicles that drove in the same direction along a roadsegment based on the feature points determined based on the navigationalinformation. Server 4901 may also update a navigational map based on theaggregated (first and/or second) navigational information.

In some embodiments, server 4901 may be configured to determine that theupdate of the navigational map associated with the geographical regionof interest is completed (or at least in a period of time). Server 4901may be configured to transmit an instruction to one or more vehicles notto collect second navigational information associated with thegeographical region of interest.

In some embodiments, server 4901 may use more resources to process oruse the second navigational information as compared to when server 4901processes or uses the first navigational information.

The foregoing description has been presented for purposes ofillustration. It is not exhaustive and is not limited to the preciseforms or embodiments disclosed. Modifications and adaptations will beapparent to those skilled in the art from consideration of thespecification and practice of the disclosed embodiments. Additionally,although aspects of the disclosed embodiments are described as beingstored in memory, one skilled in the art will appreciate that theseaspects can also be stored on other types of computer readable media,such as secondary storage devices, for example, hard disks or CD ROM, orother forms of RAM or ROM, USB media, DVD, Blu-ray, 4K Ultra HD Blu-ray,or other optical drive media.

Computer programs based on the written description and disclosed methodsare within the skill of an experienced developer. The various programsor program modules can be created using any of the techniques known toone skilled in the art or can be designed in connection with existingsoftware. For example, program sections or program modules can bedesigned in or by means of Net Framework, Net Compact Framework (andrelated languages, such as Visual Basic, C, etc.), Java, C++,Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with includedJava applets.

Moreover, while illustrative embodiments have been described herein, thescope of any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations and/or alterations as would be appreciated bythose skilled in the art based on the present disclosure. Thelimitations in the claims are to be interpreted broadly based on thelanguage employed in the claims and not limited to examples described inthe present specification or during the prosecution of the application.The examples are to be construed as non-exclusive. Furthermore, thesteps of the disclosed methods may be modified in any manner, includingby reordering steps and/or inserting or deleting steps. It is intended,therefore, that the specification and examples be considered asillustrative only, with a true scope and spirit being indicated by thefollowing claims and their full scope of equivalents.

1-134. (canceled)
 135. A system for automatically generating anavigational map relative to one or more road segments, the systemcomprising; at least one processor programmed to: cause collection offirst navigational information associated with an environment traversedby a host vehicle, wherein the first navigational information isassociated with a first aspect; determine, based on output associatedwith one or more sensors of the host vehicle, a location of the hostvehicle; determine whether the location of the host vehicle is at orwithin a predetermined distance from a geographical region of interest;cause, based on the determination that the location of the host vehicleis at or within the predetermined distance from the geographical regionof interest, collection of second navigational information associatedwith the environment traversed by the host vehicle, wherein the secondnavigational information includes a plurality of three-dimensionalfeature points and is associated with a second aspect that is differentthan the first aspect; upload from the host vehicle at least one of thecollected first navigational information or the collected secondnavigational information from the host vehicle; and update thenavigational map based on the uploaded at least one of the collectedfirst navigational information or the collected second navigationalinformation.
 136. The system of claim 135, wherein the plurality ofthree-dimensional feature points are associated with one or moredetected objects in the environment traversed by the host vehicle. 137.The system of claim 135, wherein each of the plurality ofthree-dimensional feature points include an indicator of depth relativeto the camera onboard the host vehicle.
 138. The system of claim 135,wherein each of the plurality of three-dimensional feature pointsinclude an X-Y-Z location relative to a real world origin.
 139. Thesystem of claim 135, wherein the plurality of three-dimensional featurepoints included in the second navigational information are a pluralityof second three-dimensional feature points and the first navigationalinformation includes a plurality of first three-dimensional featurepoints.
 140. The system of claim 139, wherein the first aspect is afirst density level at which the first three-dimensional feature pointsare collected and the second aspect is a second density level at whichthe second three-dimensional feature points are collected, the seconddensity level being greater than the first density level.
 141. Thesystem of claim 140, wherein the second density level is at least twotimes greater than the first density level.
 142. The system of claim140, wherein the second density level is at least five times greaterthan the first density level.
 143. The system of claim 140, wherein thesecond density level is at least ten times greater than the firstdensity level.
 144. The system of claim 139, wherein the first aspect isa first type of features associated with the first three-dimensionalfeature points and the second aspect is a second type of featuresassociated with the second three-dimensional feature points, the firsttype of features being different than the second type of features. 145.The system of claim 135, wherein the second aspect includes collectingthe plurality of three-dimensional feature points and the first aspectincludes not collecting three-dimensional feature points.
 146. Thesystem of claim 135, wherein the at least one processor is furtherprogrammed to: receive location information from the host vehicle; anddetermine, based on the received location information, whether the hostvehicle traveled within the geographical region of interest.
 147. Thesystem of claim 146, wherein the at least one processor is furtherprogrammed to upload from the host vehicle the collected secondnavigational information based on a determination that the host vehicletraveled within the geographical region of interest.
 148. The system ofclaim 146, wherein the at least one processor is further programmed totransmit, to the host vehicle, instructions to discard the collectedsecond navigational information based on a determination that the hostvehicle did not travel within the geographical region of interest. 149.The system of claim 148, wherein the determination that the host vehicledid not travel within the geographical region of interest is made afterdetermining that the host vehicle approached to within the predetermineddistance from the geographical region of interest and then moved away toat least the predetermined distance away from the geographical region ofinterest.
 150. The system of claim 135, wherein the at least one of thecollected first navigational information or the collected secondnavigational information is uploaded at a predetermined frequencyaccording to a frequency rule.
 151. The system of claim 135, wherein theat least one processor is further configured to receive and store boththe first navigational information and the second navigationalinformation.
 152. The system of claim 135, wherein the at least oneprocessor is further configured to request transmission of a subset ofthe first navigational information or the second navigationalinformation collected by the host vehicle.
 153. The system of claim 135,wherein the location of the host vehicle is determined at predeterminedtime intervals.
 154. A computer-implemented method for automaticallygenerating a navigational map relative to one or more road segments,comprising: causing collection of first navigational informationassociated with an environment traversed by a host vehicle, wherein thefirst navigational information is associated with a first aspect;determining, based on output associated with one or more sensors of thehost vehicle, a location of the host vehicle; determining whether thelocation of the host vehicle is at or within a predetermined distancefrom a geographical region of interest; causing, based on thedetermination that the location of the host vehicle is at or within thepredetermined distance from the geographical region of interest,collection of second navigational information associated with theenvironment traversed by the host vehicle, wherein the secondnavigational information includes a plurality of three-dimensionalfeature points and is associated with a second aspect that is differentthan the first aspect; uploading from the host vehicle at least one ofthe collected first navigational information or the collected secondnavigational information from the host vehicle; and updating thenavigational map based on the uploaded at least one of the collectedfirst navigational information or the collected second navigationalinformation.
 155. A non-transitory computer-readable medium storinginstructions that, when executed by at least one processor, areconfigured to cause at least one processor to: cause collection of firstnavigational information associated with an environment traversed by ahost vehicle, wherein the first navigational information is associatedwith a first aspect; determine, based on output associated with one ormore sensors of the host vehicle, a location of the host vehicle;determine whether the location of the host vehicle is at or within apredetermined distance from a geographical region of interest; cause,based on the determination that the location of the host vehicle is ator within the predetermined distance from the geographical region ofinterest, collection of second navigational information associated withthe environment traversed by the host vehicle, wherein the secondnavigational information includes a plurality of three-dimensionalfeature points and is associated with a second aspect that is differentthan the first aspect; upload from the host vehicle at least one of thecollected first navigational information or the collected secondnavigational information from the host vehicle; and update thenavigational map based on the uploaded at least one of the collectedfirst navigational information or the collected second navigationalinformation.